{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting with delayed historical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the world of forecasting, accurate predictions depend on historical data. In many real-world scenarios, however, the available data is often subject to delays.  Consider the retail industry, where sales data often arrive with delays ranging from a few days to several weeks. Such delays pose significant challenges for autoregressive models, which use past values of the target variable as predictors.\n",
    "\n",
    "One of the primary obstacles when working with delayed data is accurately evaluating model performance. Incorporating the delay into the evaluation becomes critical, as models must be evaluated based on the data available at the time of prediction. Failure to do so can lead to overly optimistic results, as the model may be accessing data that wasn't available during the prediction period.\n",
    "\n",
    "One way to mitigate this challenge is to include lags that are greater than the maximum delay that the historical data can have. For example, if the data is delayed by 7 days, the minimum lag should be 7 days. This ensures that the model always has access to the data it needs to make predictions. However, this approach will not always achieve great results because the model may be using data that is too far in the past to be useful for prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: center\">\n",
    "<img src=\"../img/forecasting_with_delayed_historical_data.gif\" style=\"width: 650px;\">\n",
    "<br>\n",
    "<font size=\"2.5\"> <i>Predictions with lags (last window available) greater than the maximum delay.</i></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.model_selection import TimeSeriesFold, backtesting_forecaster\n",
    "from skforecast.plot import set_dark_theme\n",
    "import warnings\n",
    "warnings.filterwarnings(\"once\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of time series: 204\n",
      "Frequency: MS\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-07-01</th>\n",
       "      <td>0.429795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-08-01</th>\n",
       "      <td>0.400906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-09-01</th>\n",
       "      <td>0.432159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y\n",
       "datetime            \n",
       "1991-07-01  0.429795\n",
       "1991-08-01  0.400906\n",
       "1991-09-01  0.432159"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download data and preprocessing\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name='h2o', raw=True, verbose=False,\n",
    "                     kwargs_read_csv={'header': 0, 'names': ['y', 'datetime']})\n",
    "\n",
    "data['datetime'] = pd.to_datetime(data['datetime'], format='%Y-%m-%d')\n",
    "data = data.set_index('datetime')\n",
    "data = data.asfreq('MS')\n",
    "data = data.sort_index()\n",
    "\n",
    "print(f\"Length of time series: {len(data)}\")\n",
    "print(f\"Frequency: {data.index.freqstr}\")\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates : 1991-07-01 00:00:00 --- 2005-06-01 00:00:00  (n=168)\n",
      "Test dates  : 2005-07-01 00:00:00 --- 2008-06-01 00:00:00  (n=36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-validation dates\n",
    "# ==============================================================================\n",
    "end_train = '2005-06-01 23:59:59'\n",
    "data_train = data[:end_train].copy()\n",
    "data_test  = data[end_train:].copy()\n",
    "\n",
    "print(\n",
    "    f\"Train dates : {data.index.min()} --- {data.loc[:end_train].index.max()}\"\n",
    "    f\"  (n={len(data.loc[:end_train])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Test dates  : {data.loc[end_train:].index.min()} --- {data.index.max()}\"\n",
    "    f\"  (n={len(data.loc[end_train:])})\"\n",
    ")\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data.loc[:end_train, 'y'].plot(ax=ax, label='train')\n",
    "data.loc[end_train:, 'y'].plot(ax=ax, label='test')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting with delayed data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data used in this example is a time series of monthly values. Let's assume that the data is delayed by 3 months. This means that the data for January will not be available until April, the data for February will not be available until May, and so on.\n",
    "\n",
    "Ideally, we would like to forecast the entire next year using the last 12 months of data, starting with the month immediately preceding the forecast (lags 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12). However, because the data are delayed by 3 months, it is not possible to use lags 1, 2, or 3 to predict the target variable because these data are not available at the time of the forecast. Therefore, the minimum lag must be 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and fit forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = HistGradientBoostingRegressor(random_state=123),\n",
    "                 lags      = [4, 5, 6, 7, 8, 9, 10, 11, 12] \n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data_train['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a backtesting process, three years are forecast in batches of 12 months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "To a better understanding of backtesting process visit the <a href=\"../user_guides/backtesting.html\">Backtesting user guide</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of folds\n",
      "--------------------\n",
      "Number of observations used for initial training: 168\n",
      "Number of observations used for backtesting: 36\n",
      "    Number of folds: 3\n",
      "    Number skipped folds: 0 \n",
      "    Number of steps per fold: 12\n",
      "    Number of steps to exclude between last observed data (last window) and predictions (gap): 0\n",
      "\n",
      "Fold: 0\n",
      "    Training:   1991-07-01 00:00:00 -- 2005-06-01 00:00:00  (n=168)\n",
      "    Validation: 2005-07-01 00:00:00 -- 2006-06-01 00:00:00  (n=12)\n",
      "Fold: 1\n",
      "    Training:   No training in this fold\n",
      "    Validation: 2006-07-01 00:00:00 -- 2007-06-01 00:00:00  (n=12)\n",
      "Fold: 2\n",
      "    Training:   No training in this fold\n",
      "    Validation: 2007-07-01 00:00:00 -- 2008-06-01 00:00:00  (n=12)\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e87ec136a6654e469e7ea05b84852328",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_absolute_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.065997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_absolute_error\n",
       "0             0.065997"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting forecaster on test data\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 12,\n",
    "         initial_train_size = len(data_train),\n",
    "         refit              = False,\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster            = forecaster,\n",
    "                          y                     = data['y'],\n",
    "                          cv                    = cv,\n",
    "                          metric                = 'mean_absolute_error',\n",
    "                          n_jobs                = 'auto',\n",
    "                          verbose               = True,\n",
    "                          show_progress         = True\n",
    "                      )\n",
    "\n",
    "metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.077316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.070779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.088995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.101434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.122724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred\n",
       "2005-07-01     0  1.077316\n",
       "2005-08-01     0  1.070779\n",
       "2005-09-01     0  1.088995\n",
       "2005-10-01     0  1.101434\n",
       "2005-11-01     0  1.122724"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest predictions\n",
    "# ==============================================================================\n",
    "predictions.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data.loc[:end_train, 'y'].plot(ax=ax, label='train')\n",
    "data.loc[end_train:, 'y'].plot(ax=ax, label='test')\n",
    "predictions['pred'].plot(ax=ax, label='predictions')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting in production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the model has been validated, taking into account the delay, it can be used in production. In this case, the model will use the data available at the time of the forecast, which will be data starting 3 months ago. \n",
    "\n",
    "The way the model identifies the data to use is by position index. For example, lag 4 is the value at position 4 from the end of the last available window. The forecaster assumes that the last window provided ends just before the first step to be predicted, but because of the delay, the most recent data available will not be the most recent data in the time series. To ensure that the lags are taken from the correct position, the last window must be extended with dummy values. The number of dummy values must be equal to the number of steps between the last available data and the date just before the first forecast step. In this case, the lag is 3 months, so the number of dummy values must be 3.\n",
    "\n",
    "Let's take a real example, first we train the model with all available data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and fit forecaster (whole data)\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = HistGradientBoostingRegressor(random_state=123),\n",
    "                 lags      = [4, 5, 6, 7, 8, 9, 10, 11] \n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-08-01</th>\n",
       "      <td>1.078219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-01</th>\n",
       "      <td>1.110982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-01</th>\n",
       "      <td>1.109979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-11-01</th>\n",
       "      <td>1.163534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-01</th>\n",
       "      <td>1.176589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>1.219941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-01</th>\n",
       "      <td>0.761822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-01</th>\n",
       "      <td>0.649435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>0.827887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-01</th>\n",
       "      <td>0.816255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-06-01</th>\n",
       "      <td>0.762137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y\n",
       "datetime            \n",
       "2007-08-01  1.078219\n",
       "2007-09-01  1.110982\n",
       "2007-10-01  1.109979\n",
       "2007-11-01  1.163534\n",
       "2007-12-01  1.176589\n",
       "2008-01-01  1.219941\n",
       "2008-02-01  0.761822\n",
       "2008-03-01  0.649435\n",
       "2008-04-01  0.827887\n",
       "2008-05-01  0.816255\n",
       "2008-06-01  0.762137"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last window \n",
    "# ==============================================================================\n",
    "last_window = forecaster.last_window_\n",
    "last_window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our latest available data date is `2008-06-01` and, as we know we have a 3 months delay, this means that we are actually sometime in September (the time at which we want to make predictions) and our first predicted point will be `2008-10-01`.\n",
    "\n",
    "Since the forecaster expects the last window to end in `2008-09-01` and the last available data is the `2008-06-01` value, the **last window must be extended by 3 dummy values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-08-01</th>\n",
       "      <td>1.078219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-01</th>\n",
       "      <td>1.110982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-01</th>\n",
       "      <td>1.109979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-11-01</th>\n",
       "      <td>1.163534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-01</th>\n",
       "      <td>1.176589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>1.219941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-01</th>\n",
       "      <td>0.761822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-01</th>\n",
       "      <td>0.649435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>0.827887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-01</th>\n",
       "      <td>0.816255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-06-01</th>\n",
       "      <td>0.762137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09-01</th>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y\n",
       "2007-08-01  1.078219\n",
       "2007-09-01  1.110982\n",
       "2007-10-01  1.109979\n",
       "2007-11-01  1.163534\n",
       "2007-12-01  1.176589\n",
       "2008-01-01  1.219941\n",
       "2008-02-01  0.761822\n",
       "2008-03-01  0.649435\n",
       "2008-04-01  0.827887\n",
       "2008-05-01  0.816255\n",
       "2008-06-01  0.762137\n",
       "2008-07-01       inf\n",
       "2008-08-01       inf\n",
       "2008-09-01       inf"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummy values to complete the last_window until the moment of prediction\n",
    "# ==============================================================================\n",
    "# These dummy values are never used by the model because they are always posterior to the\n",
    "# smallest lag.\n",
    "date_start_prediction = pd.to_datetime(\"2008-09-30\")\n",
    "dummy_value = np.inf\n",
    "\n",
    "last_window_extended = last_window.reindex(\n",
    "    pd.date_range(start=last_window.index[0], end=date_start_prediction, freq='MS'),\n",
    "    fill_value = dummy_value\n",
    ")\n",
    "\n",
    "last_window_extended"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "Dummy values are never used by the model because they are always posterior to the smallest lag.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2008-10-01    1.107654\n",
       "2008-11-01    1.182545\n",
       "2008-12-01    1.153173\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predictions\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(steps=12, last_window=last_window_extended)\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tsO/xl+DvWfOVx1x604P4/r1Hk47h0P12VgQ+CeqaOnXbcclyyptXaWxuVjyCFH3v7DmysjIVUU3Rcmoade2dj+H5+6/Hbfc9h8r16t/W1jfg2jsfRygUwoIlK5Tdku23nKAIcxo7Rde/+P4PNDY1Y+XqiqTlMxmGYZguEB7qUEgRe7YIcxLbfUuAUBBYPjfxYyLdP520smgR8/VrFGHueMTcBXXMK/uq9cvLauajzl9su5Vlm3wgwwMcXRJmYZ6uMP9lykwMmnRg0t/Hi+0jTr/G6Ev0WGjHQM/U6XMUm4dXu0j8PXtBzO/HbjwCYzYcjsP22zmmiofP51N2KkYOK0d7ewDTdX9HAlgI7kSM23gDzJyzqNPHdMWGI4Zg9rzFETsN8ce0f5RxjRxeHhHmJNhJlAvWVq7H2A2HK99T06kVq9fh14+exDc//4lvfp6Kz77+NeY5GYZhGIMR89oKoKi/kgBqORQJJ6pWA+1JbDOi+6cbIuYkzEeMdzZiTlaa3D6OCvPWjFwsL9tM+b6sdj7qi7dQvg/Z1PkzyxPGIC0F4sDiMC5bSq4HFueCHtF/teC3nlEXW/izBXk52Xjprc/w9Kuq31sPRZhJmBulxUbhSz76GMJheLWkG4qS733sxYrdZedtN8Xl5xyv+Nspkl9X71DmPsMwTHePmJP4LOqPsD4qa7UwX7Mk+WMiEXMHhXlfTZhXr1VvnUz+JL+9/vUdSP78dcxpaM4uRkHjapRX/o0lRZsq94dtWiQM1RUQGpkNjM0BZjfb8tLdgh6haMnzbeeXWTbdZOOYnzfbZGMsXrYqJqqsZ8achdhogyGK1ST+i/zkCxavUGxC5NcWkFgvLNCtxuP4Z/4SJScg2WOo8oqI4Cdj/uLlGLvRCMVrLthy0hjFArNwiZrdnQrBYEip0PPv+57D7kdegCED+ylWF4ZhGMZk8idFrwk7I+Zr1cIGCWmo0SWjOgCJzYKS6KJFazBkKTT3+50ODBjRub9cjM9GlvTbCgvLd4YnHMROMx5ERqgt6jG3KflzmE6YEwcWcQHlHifMuwvlA0px46WnKeKZKtWceuwBeOqVjtFwwcPPvo0tJo7BbVedpYhpSrrde5etlZ+FT/3rH6fiP9edh03Hb6QkcVISZnNz8qj4e59+j4qqajxz77WKmB5a3h/77b4dNp+gLhpWrFqn3EevV1xYgEx/x02Vdz/5Dq2tbbj/1snYeORQJer97yvPwlsffxuxsXTFHjtuidOOPVB5HcpJoMRYSkCl/4lhGIYxQIY/KvDWq8Jc8Zi7QZgHtLrYTnXsJg88CXGy2ghbjdVieMzWwMSdgG0P6Pi7+J0MG/3uzZkF+Hmcqh82Wfw++tXMU4cQDtrqMR+WpQrxYDhqZ2F6mJWlu/DWR98gOysTH7/0fwiGQooof+ntzzqNbh92+tW46vwT8e4zdyr+8iXL1+CDz3+IPGbyjffh7hsvVCqtVCrlEl/CFeeVJn1OirQfc84NuPGS0/DigzciI8OHeUq5RDVh9OMvf8K+u22LN5+8XYmqi3KJesgHfty5NyrlEj95+Z6YcompQnaVfXffFpecfSyyMzOxaNkqnHv13Zi3cFnKz8EwDMPo/OVUDrBaTfi03MqSnRf1bq/t5LpNiaGEUzWyReJnzTraprWnjjnNDVESrWDnhoj5n6OOQUtWXxTVLcGm89+I3O+xuSqLiJh/VA0cXAxs1Qfo7w9jbTv7zAkW5jZCopiqslx9e8eqKVvTtlcCqNrKsefckPQ5qXrKSRfeEnPf2x9/E/NzfLIu+dOT1QsnK0ui38U/x5wFS3HUmWpJxUQkqrhD/zu1uCV+nzabE4MZhmFk2ljaWqOe7j4We7r7D1VvaSHQ2knpY1osEE5VQtlAs0fWVETHYrWvW/j9iweofnLxukSuc8J8XZG6M77ZgtfhC0cbBoqIedhjrzD/ud6DQZlhbNkH2K8ojGfXsTBXjoctR4FhGIZhGGsj5m3NUU93di5CPgtbpfdXK2xhbSeJn0TQwYj5hpsBW+2jfj/9B7WUpB1Jjlna8fBlAIVxO9hiJ0N0SbUp+TPoyUBNnlowoqRucczvvCF7rSzDNSvL0lbgo/Xq/88+8ygszBmGYRimu6EXUSJC29aiRq/b1TyjYHx0ViYDUvCXx1hZbJYbVDLygDPV7//4HzBvin1jEceDKB6YWJjb5XfXqMsbgLA3Q+n0mddSGfM7SgS1M/lTVGVZ2urBh9WqMN+jL5Btc7NIt8JWFptg2wbDMAwjhaMuBcqGAE9cqYpwEaFt1WrOUdS8qL+1wjyVxE+nPOa0U3DYBcquAZbPA75+Tb1fs5RY7jHXC3PymS/8u6PHnCxH/YbatmCp7qNaj4rql3eoGC6qsthRLtHvCaNcq2FOEfN17UBdACjIAIZmAvNiq0b3SjhizjAMw3RbQqXlqN1kZ9tqMDsO2SM22AQoKAZKyztGzAnNzmKZMM/IjEaC13QlzIXH3MbjM2y0Knqb6oH3HoouDsRYrBbmYqGUKGKeKyLm1bbOS3W+KswLGzom6kbLJVofqx2cCXg9QHNIFeXUWKhJOyxZrEgVeBoYhmGYbktwl6OxfttDEB4+Dr0CaikvhKWwRSQR5gGrKrP0G6JGeul1Gms7f6xmk7BVmIv/e83iqOee0FlZwnZGzGPGpi2WGqptbXYUiZg3LO/wOzvLJQ7XbCxLlFNVjd23aQcjk3M/FViYMwzDMN0XraNkWB+l7MmI8n8xwjyBlcXKiHmqNpaYiLmNciOnT2yCpUBfHcVKQSyOh6jMEn1RnZVFWzDYZmUZotwW1XeMmHsiyZ8ZttUwX9YWva+VI+Yx8DQwDMMw3ZawqBntZJt1OynSCfM+fePKJbbYK8y7srE45TFPJsz1XbatFMT6iDk1esrK1caVF50HETG3YV7afVmoz+2fVJhHyyX6bCuVuKQlGh7niHksveRKxjAMw/RIKMGP6C0e884i5vFWFquEeaoVWRyLmOert+QxT7RIsPp8Ebs3Qa1WeMmAjqUSqROpTfNSmzdYWbhmt9Ygp70uucfca58wX6ZrUM4R81h4GhiGYZju24ren+VsAxtHhXlBnMfcJiuLGEPVqq4fK8SwsqPhcU/E3KodFkrOpS9i3fLYBFBxvBrrbN1JWJ+f3F9ud7lEYWVZohPm7VrE3M8Rc4VeciVjiGMP2ROvPhrbJZRJnXtvuRjP3Hutba+3y3ab4YvX74fHpgYUDNPtEDaW3hQx11tZ8grjPOZxVhYROZaKJ2rNiBe+iYiJUnt7vpVFb2NZsyQ2AVREzClhNhy2zYLVmb88tlyiHR7zaA1zQas2FVn8UafAdcxtoqykEBecdiR232ELDOxfivqGRixZvhpvf/wt3vzwazS36JaPFpCV6cfl5x6Ps664K3LfpWcfi0vPPk75PhAIoq6+EfMWLcMnX/+CF974BG3t0Za9jP18+/OfuOK8E3DYfrvg7Y+/cXo4DOM+eqMwLyzrxGOuRcyDSh06hEXkVib0WkLUtjR1/fh4+4j+Z6vIzU+ycAirCaAkhq06X4Qwp/ryYkehOF6Y6yPm1gvzmi4i5tHOn9ZKQh/CGBwR5tH727T1UqbSYMiD3g4LcxsYWt4f7z/3H0X43vnQi/hn/hK0tbVjzIbDcfzhe2PNuip8/t3vlo5h/z22R0NjM/6Y9k/M/XMWLMXRZ10Hr9eLor752G6LTXDRGUfhiP13xeGnX4PGJu1CzzjCGx98idOOO5CFOcN0Kcx7wQYwCTth3SGEVSW+XGLQQtEn5pw80toCoFPsSrhMJWIuxuPzWjcWfbMnIcxFxFwsGJpqba3vHomYJ6hhrgzBJisLNRbK8Kie8jW6U4cj5rH0giuZ89xxzTkIBoPY57jJ+PDzH7Fg8QosW7kW//v2N/zrgltiRPmZJxyMr958EAt+eRNTPnsGt19zDnJzoltjRx20O/754VXss+s2+PGDx7Hot7fxyiM3Y1D/0k7HcPA+O+GL7zuKfxpXRVUN1lasV0T6M699hMNPuwYbjxqG8045PPK4VdM+VF5TD42DxkMMHtRPecyBe+2Ad5+5Ewt/fQufvHwPNhg6CBPHbYhPX74H835+A889cD2Kiwo62ENoN+Hvr15QnnPymcfA5/Pi+smnYNZ3r2DK/57F0Qerr0O88cS/cdtVZ8WMhZ5zyR/vYIetJnT4H2kMNLZRwwfH3H/GCQfj5w+fUL6nhcn/3XgBfv34KWXsP7z3qCKIO+O3T57C6ccfFHMfWU9oJ0JQkJ+Hu2+4ADO+fglzf3xdGfvYjYZHfk/fv/r4rZj70+vK7z975V5MGDsq+nzf/YFJ4zbEsMH6klsMw8QIsN4SMac280RTXVQEklCPT/7UIqCWWBOEMG9pTO3x+gi5FRH8zs6L+ORPO5oM6Y9F1erocaPzs3xU1MpiU8S81d8HTdklyveF9Uk85jYlfw7PjiZ+hnWR8WjE3NKX7zb0iGnIDQeTfmXp65Z28djsFB9rBIpC77ztpnju9U9SsquEwmFcf9cT2OXw83DR9fdhhy0n4LrJp8Q8Jic7CxeefhQuuu4eHHzyFYr4e/Suyzt93q02HYO/Z81PacwLlqzANz9NxX67bQujXHb2cbjvydex97EXK/aYh++4DNddfDJu+O+TOPSUqzBsyEBcfs7xMX+z/ZYTMKCsGIedejVuvvtpxXLzwgM3oKauAQeceClefPNT3HXdeRjYT724vPruFzhk352R6Y9e5A/ff1esWbceP/4+vcOYFi1bhWmz5uPQ/XaOuf+wfXfGu59+p3zv9Xqwel0Vzrz8Tuxy2Hm454nXcPUF/1IWGunwxH+vRGlxXxx//k3Y57iLMfOfRXjj8dtQWKB+cDx4+2VYvbYK+x1/qfL7h559C4FA1EK0ck0F1lVWY+vNeknzFIYxQm+LmAt/+drlqlVCRNFFxFzUMddFzPUCSGoVnFSFufBS21XSkuZCLAASRcytbnik372oqwICbWqS8p4nAMPGqj/PnRqtqW7xnIhoeZ/mdcgMJt4Bt6tcokj81NtYiNaweo5yxFylR1zJGiumJf16u3ZhzGPXVUxP+thPa2KF65LKmQkfZ4ThQwYq0VgSu3pmfvMy5v/8hvJ17UUnRe5/6uUP8POUGVixah1++mM67nr4RRy0Z6w4zPT7ce0dj2Hq9LmY8c9CXHz9fdhy0lhMGr9hwjGQcO+b3wdrKtanPG6K6g8ZpEVnDPDYC+/iu1/+Uv7+6Vc+UKLl9z3xumKhmTV3Ed54/0tst2VsVLumrh7X3fUEFi5didfe/1L5W1p8PPj0m1i8bDUefOYttLcHsNWmY5XHf/LVz8rt3roI/lEH7q7YPpLxziff4pB9dor8LCL5736iCnNaRNz96CuYPnsBlq9aq9z/+vtf4sC4uTfCVnRMxm2kiH16Xvpfbrn3GdTWN2D/PbdXHlM+oAw//f43Fi5Zofz+oy9+wux5WsKQBu1mDB6o85UyDNM7PeaiGkrNWqChNirM4+uYx0SpfRZFzFPwlwtE2UA7jpGIlpPVhkRwPKGwPcmfdCxoUbJ+rfrzZtqu7xcvqxYXC60sAa8fiwZsh2VlW2BF6abKfUVJouV2eswjNcx1iZ8ER8xjYY+5Q+x3wiWKYH/o9kuRmemP3L/j1hNx/qlHYtSIwcjPy4HP51NEKn2JiDuJVIoAC0j0U3R5wxFDMG1mx6h4dlamctvaloIfUIMKgYRNNC2ePT8qKskiQ/yzIHpfZVUNSopj20TPXbgMYV1Uhf5u7sJofdxQKITqmnqUFhdG/g/yXB9z8B6KNWiT0SMxetRQnHzxrUnH9f5n3+OGyadis002xp8z5uKw/XdRxLJ+wXTy0fvhmIP3VMRydnYm/P4MzJq7GGYZu/Fw5OVmK3ac+OMxXLOmPPHSe7jz+vOUHYAffvsbH37xI5auWBPz+JbWVuX4M0yvZoMJwF4nAh8/BSyfG23YohHuDRFzkfhZU6HaISiCrkTM460susR9r0XCvNWAMFd83TbtakQSP+tTKN9oscecIDtLPzVqjdm/AtO0fCELrSwzhx+IPzdSCzsICpP4y5UhCCuL5RHzjjXMCfaY90Bhnlc2KenvgnHbeP3KOnqQBaG4xw4vHZ/22KjyCgnLeH8zecyJltboip582s8/cANeIOvGQy+iprZeiRLfc/NFikg0W7mFRC2NQdgnUoFEvhgjQX8fjz+j4+mjt2EIYU/R6Oh9qm0k9m9i7UH0d+265xH3eXR/98o7nyt+brK3kP+cdhdWrq5I+v+Q2KfHHLrvzoowJyFMlWcEB++9I66ffCpuuecZTP17DhqamnHOSYdhs002SvqcoVC4QynDDN2c5OXkYG1lNY44/ZoOf0uJwMQ9j72Kj/73I3befjPsuv3muPSc43DOlf/BZ9/8GnlsYUE+qqo7NoVgmF7F6K1Ur+5Gm+uEue6aZkMNZtd4zKvXqcKc6FuiWiUSWVmsFOapWlns7v7ZWeKnMhb1syxsh5WFqFyp3lavBT59xpY5WdZvS+U2v2kNAr4sRXAPXxP9TElax9yCiPmGgRYMCLVjsS8Ls5oy8EUNML0pLmIuOn/2grV1rxHmTQYuyFY9NhnVtfX4/tdpOPmY/fH0qx92Kq4njBmliNab/+/pSAQ5kceZRPrEcaMi0fGRw8oV0T1/ceKtKhK58xYtx4YbDFFsJl1Bi4hdtt8MDz3zVuS+qupa9C8rjvw8YujAmKRUu6FE1b9nL1Cq2pDIvu7Ox7v8G7KzkN/9vc++wzCqlPO/HyK/23LSGEz5ew6e14l1EdVOhjInpUWRn/vk5WCozv4zY85C9CspQiAYVKxJyVi8bBXmLV6OJ156H4/ccZmyEyCEOZW5HDZkAGbOibVkMUyvo6AotuSc3u9shWUjVSbuDPQbCnz1ivWlACNWFp0w19c1t8XKYtBjrh+PHRHzSOJnEmEuvN1xASJpxDd7mvqlegymfx89PhZ2RG32F6Cy70jl+/1/uw65rerOdWdE65jLfw/Nz8jGfKhzct9q9SueiJXF4oj5RtlhzFMOgbtD87w+sYGrb38UGT6fUnHjoL12UGwqJKapPjWJ4GBQPSuXLF+l+MdPPfYApcQiJTSeeOS+HZ6vrb0d/77yLGw6fiNsMmakUtmERGUiG4u+JjZ5nuMhqwzVWCfRPXrUMJx6zAF4++nbFQvHI8+9E3ncT79PxylH74/xG2+gVA2589rzlHE4ySvvfo7zTjlCiVp/+vUvXT7+k69+QZ/cXNx57bn4+Y8ZindbQP7uiWNHKYm65D+nBFTyoHcGzQkdI9rVoLm7/9bJCOo+EGlBNnX6HDx777XK89KOyBYTR+PK809U5pAsLf++6ixss/l4lA8sUxYH9Jr6BdZmEzZWSmtSPgHD9GryixMIc31VFoc+znY9BthiT2Cjzax9HRJ8onNkjDDXAgiUDKovYGCVr9tUxNy+0oAQTZWSRswtjt4LK4sQ4WSp+e4tdZdDj0XJn6tKJyrPWVy3OCVRbme5xGS02WBl6esLY9rEEBZuFkJxhnGbrp30iIi52yHP8F7HXIQLTzsSV194Egb2L1HEFkWxKVlSRGkp6e/Gu5/CeScfjmsuOAm//jkTdzzwAh687ZKY56Oo+8PPvq1UPBnQrwS//zULl970YKdjePW9L/DZy/civ08u6hui3kASlH9/9aLaYKihEfMXLVeSLeMbDN18zzO49+aL8O6zdypJpDf850lMGKuuyp3ivU+/xy2Xn6H4x1Pxz1NNdioZedDeO2LyjffF/O7Ftz7F+NEb4LH/XKHk67z32ffKcdlth82TPt+Dz7ypLKCoggzN6X8eeUn5Wc8J59+Mq84/UbEjlRQVoKKyRjmu5LWnBRlV7fm/Wy5S/PPra+rw6Vc/K0mogkP22RnvfPKd5Q2oGKbbCHPRVCfOY+6IlYUsJGIMm+wIzPnDen85lQAky4pI/izWhLk+GisEKFUnsUyYG/GYWyiGaTy0CBFVakTEvKVzK4t15RLjKuR0NSeSS0iuKFWtvYMru94dF3hsSv5MBtU1J/wWrq0PLg4rVpm6ILA+4O6IOQtzm6CSd1R5hL4648mX3le+9CRqLkMR4lSixAIS3F/++AdOOmq/iEXl/x57VflKBYouH3fujTH3jdkxWq+brBqDJsXW/f5lyswO97314dfKIkEw+YZYgUwk8mRvvd/pHe6j2uVk9aDyialy9pX/Ub7ioUXI5BvvV7703PHgC0nHSg2bzrnqvzH3URfX+MXA9f95QvlKxHlX3w2/z4f2YLBDqm1xYQH232M77Htc7MKMYXodJHZEJDImYu5wuUT9WCg5lX4WkWzZFPaPRssJ8ToFJYmFoEXCz5yVxaKIeUYmcNZ/1Oj4E1d2XcM8xspiQx3zztDnbSnVFmREcT1YqQnzcgMV5ETEnBYrVF7TY6LwQzKer1uMslAAV/QZjJkZ2tw4EDE/skR9kbcq3S3KCbay9CJuvfdZNDV1cbHoBmRkqPabK887QUnkJC93T4SsL9fc/qhSvpFhejUiWi6ElxB4TpdL1AtzEnrj1TKoliC85PHCXAjMeCGoJYBK9w2LOW92gceckmGpCgt11hQWFqetLPHJn8nQ5wHIGsuAYWjJ6gt/oAn9aual/GfCY64MS/LO005tDdi3rQ65cX1i7CyXWJwRxh7aW/XNKhbmjIugqDZ19uzukBeb7DfUEfPKfz+CngqVc/zg8x+dHgbDOE9+NMlagbzWFGXUJ386HTEnNkmvIVlKVhbhVY6PzCeyslhax9yAMI9EqSWPJV8toRszP11UZfFYbWWJL5eYDL1QlXXu0q4NgIFVM+ALx1Y2S1mYS7azFGjR+LpO5tvqcokHF4cVm8y0RmBei/uFOVtZuhlvfPCV8tWbSWSRYRiml0TMibxCtYGM/sPeCX+s8LsvnQ0MGgmUDQYGjgBWm+9/0HXEvMKYMPe6wMoSSUSVLIb7FMUK89WLdMK8KyuL0xFzvZVFrjAfXGmsEaIol6gMi84XWcWFwmHkR4S5L4WIuTVJmUcJG0s3iJYTHDFnGIZhul/EXG9jcTpivn4NMHdKNAnUCnK116qvVm8D7bEJmPERWlHL3BXJn1ZFzPXCXFu45KZWx9yWzp92WllowVQ+Svl2cEXqiZ+ERxe9l2llyUYYon1ifSfPa2XEvDQjjF27kY2FYGHOMAzDdLOIed/YiixOdf4Uwpyi1zM029nYbeUnXMZYJHSCWB81F3WzBcKeINPKQqJTiEg31DGPEeZlqSV/Wl1TPb6OeTL0yZ4yxjJsnPI8hQ3L0ael0tCfenS7GjKFubCxEA2d7ApEGgxZoJsPLQ4jwwNMbQAWdgMbC8HCnGEYhnE3BcWxlgiykOhrmDud/EmlC8nOQiX7aMEQv5CwyrscI8xjI7QeKyLmIlpO0fpAtGu1cx7z4lhhTlVa/FmdR8wj9cN9znrMZdeaLytXbvpVm+t5YUXJxPxQ1F8ejuuSrac1pP4uywJFeqhmY+ku0XKChTnDMAzjbkRktGJl0oi5I1aWPoVRgUwRUBKsVo2lK2GerFyiVGFuwl9u1Vj08y+sLCJaToI3mZUkYmWxQKhRVFgsDLqysshuMqT979ltdekJc4kLllyEUO/xduovJ9otiph7EMZW2inxRU33Eeac/MkwDMN0D2G+ZrFSEk4R5vEecycaDOmtLFbX6xbP2ZZaxNySqixm/OVW+rr1Vhaq5y6ScZNFy5WxWFRGUm9jSVWYy5wXrUxkdrs5YQ4LIuYzMnJRULYpPF3UaG+1qFziqGygMANoDgGzUtjAcAscMWcYhmHci88P5Gqt6NcsUW/1wlwIVSc95g01cb7uDGui5RRhbdN1ARbdPzupY26JlcUNEXN6LjH/NC90/PsP71qYC5FoRblEcZwoYi9sKnbNixYxz2pL4q1POWIuP17bmY3FSo/55n3UJ/67EQiEu0/EnIU5k5BV0z7EPrtuk9ZzHHXQ7vjnh9Q6i3ZnfvvkKZx+/EFOD4NheiYiKkr+7apVOiuLtkfdWG9Nve5UoqP+TG0MddZWQonYWEh8hw1FzMMyFwmmhbkFEXNhYyH7UNVq9XsqVZlixNyShZyImKfiL5e9wyKsLO1mhbk6lpADuRoiYi7bY76FdrpOaeg+orzHWFlO3dTgtlqaPPOXrqlFCtx7y8Xom5+HUyffJn0s224xHm8/dQdG73gM6uoNXiw7YeLuJ6K2rpOLG8MwjJ3CnMoEigixEjFXr8Oe5nqEqca33VYWIQxJhIlESKtEX6TNe5zgc8pjrq8MkwpiLDKPkTgvaLeCaruXlkeFebKKLMpYLKxjnmqpxMhYJJ4vaUbMI1YWicfo4NYanNVcgS8zC3BPbn/HIuZTupmU6RHCnJFPRZW2NcswDOMkovJG3XqgqTYqEIUwa6pzxsoS7y+3UvRlJYnEdhoxl1jxw41WlogwrwZqtG6o1OCpSyuLhXXMUy2VaGHyZ5bpiLl8j/nGgRbs21aHtV5Rzdy+iLkXYWyqna5TG7tXxJytLC7gzBMOxldvPogFv7yJKZ89g9uvOQe5OdEkkvKBZXj+/usx+/tXlcd88/bD2G2HzTF4UD8lWk7M+eE1xX5C0flEzPj6Jey/x3aRn794/X789cXzkZ+3mjQWi39/BznZWR2sLPQ69PO+u22LN5+8DQt/eQtfvP4ANp+wcQfryh+fPqP8/ul7rkFRXzUZRc8JR+yDnz58Akv+eAc/vPcoDt9/18jvbph8Kp5/4IbIz2QPodfdZbvNIvf99MHjOO7QvVK2ztD/QM8hGLvRcOV/mPfT65j74+v47JV7MWHsqJh5ePeZO7Hw17eUY3HrFWdG5oQoKeqrHAv6/a8fP4VD99s54VgYRhrCLtFb0QswSjoUlU9KBik3HhEdtXsLPt5fbqXHPBIxjxPfMR7zJA2GXJH8acFOQh/dTorohirOgWRdP3UC1FKPecoRc0kLOTrftNfONusxD8qPmEe7fnY+11ZEzEfnAH18QEMQmNuNEj8JFuYuIBQO4/q7nsAuh5+Hi66/DztsOQHXTT4l8vvbrz4HmZl+HHbqVdjtiPNx233PobGpBavWVOK0S25XHrPDQWcp9pMb/vNEwtf49c9Z2G6LTZTvyVYzasQQZGdnYtRwNcKwzRbj8fes+Whu0SUWxXHV+SfisRfexZ5HX4hFy1bikTsvh8+nnkKbjt8I/3fjBXj29Y+U3//8xwxcdMbRHUTyDZedhidefA+7HX4+XnzrM9x780WRcf0ydQa22nQMvNrFe9vNx6NqfW3k9wP6FWPE0EH4ecoM03P90O2XYfXaKux3/KXY57iL8dCzbyEQUD9Mhw0egJcfuQkff/Uz9jjqApx95X+w1aZjcfvVZ0f+/r5bL8agAaU48oxrccZld+Dko/ZDaZGuZBfDyIQ6+U1+DNjxUKC31zCniLnez01VOPQ/2x0xFxVA9FFryz3mcQpD7BYkqmNuiZVFE+bNJj3mMhcJeotTtRYxF3TqMZe4q5HhBw4+Fxi/vUmPuaQFi8i3CIWQGTBp7bUgYi4aDHXW9VNWxPygojCWbx7Enn3DMTaWvxqBkNpCqdvAwtwFPPXyB4rYXLFqHX76YzruevhFHLTnDpHflw8oxR/TZmPOgqVYtnItvvzhD/z25yyEQiHU1Kmr48rqWsV+Ut+Q+E35y5QZ2FYTuNtsPh4z5yzEL1NmKh51YrstxuOXqTM7HSeJ8q9+mIJFy1bh7kdfwZBB/TFiyKBIdPubn//EI8+9o/z+6Vc/xHc//xnz92efdCje+vAbPP/GJ8pjnnjpfXzy9S/K/cRvf81Gn9wcjB+9gfLz1puNw+MvvhcZI41/1dpKLFmuJfqYoHxAGX74bRoWLFmBxctW46MvfsLseWqlhwtOPRLvfPKdcjzod1P+nqMsmI44YFdkZfqxwdBB2H2HLXDZLQ/hzxlzMeOfhbj0pgeRkxONqDOMVMo3VKNhw8YCvd3KUi+EeazNzhOxsjgUMdcLc6tsEsmEOYmpP78CFkwDaiptqMqSZh1zKzzmSsTciDCXGL0fvBEwdhtgpyM639mwepEghHlLg1K72y11zAu090NXdcxlRMz3LQpjYCZw1zB6zTC26NM9Ez8J9pi7gB23nojzTz0So0YMRn5eDnw+n2KfoC+KYD/z6ke445pzsPO2m+KH3/7Gx1/+jH/ma2XDUoRE9y1XnIHiogJFmJMoX1dVrUSjX33vC2wxcYwiqjtjtu4111WoH5IlxX0VkbvhiMH49OtfYx4/Zfoc7LJ91IZCUfrX3v0i5jG04Dj9OLWiCSWvkkimMbW3B5Svl97+DJeec5xi7aEI+q9dLB664omX3sPdN1yAI/bfVZnLD7/4EUtXrFF+N3bjERiz4XAcprOneDwe5XgMKe+PkcPKlTFNn70g8nv632s4SZaxiryC2OiwW6CFwp4nAJ89B6yYZ+1r6QVYvBAmNCtL2CmPud5OEhHDFllZEiVd/i9qSUzsMe+pVVl0FqfauEVJKh5zGVYWsVDpW6Iunowmf0bG4pFSwxxN5j+LrCiXGLWydBEx14S5zwP4EEbQRIQ7S/uTCXnAXn2pIkv3TPwkOGLuMOTfJl81Ce0zLr0D+xw7Gdfe8ZjyO79ffYO88u7n2PaAM/DWR99g9Khh+PSVe3DqMQcYeh16/praBkXc0hdF6CmKTiJ90rgNkZHhwx/T/un0OYTlgxBrcq/k7mk0LoqMCxFOonfB4uWKpYTu6yyqTzsInrg3NP1fev7vsVex6+Hn4csfp2D7rSbg23ceiXjp83Ky8dJbn2HPoy+KfO1x1IXY7sAzsXS5Kt4ZxlZE/W4Sp+l+eMtk633VRLsNN7X+tToI8zr3RswjHnOLrCypCj5lLFZ4zF3U+VN/XlApTf1xSMnKIkH+ZOkqtFFVGMMe86DciHln/3eXY5FfLrFA+//qu5jrNu2QpNNkKEv3d1eWhzCxmyZ+EizMHWbCmFGKuL35/55W7BFk8ehfpm3d6iALB3myT7/0Djz+wns47jA1AZIiuIQvhYvMb3/Nwt67bIONRg7F73/NVqLT5F2nhEyKAnfmL++K+YtXYNNNNoq5b/NNYpNDSWBvPnFMzH1bThqLeYuWR34mMU4+8x22nhjxktPtIfvuhJHDB+OXP5L7y6uq69AnLycmWXPcxqotRg/N8ZMvvY9jz7kBn371M445eA/l/hlzFmKjDYYoVpn4r/ZAAAsWr1AWS/pkUYqiFxZoF0WGsSpirjRTKXRPMuqwMfaIYXp+UZYwYmXRR6gDUXuHK4R50F4rS2do0ftwT0/+pIg5IRJAU7ay+OTtZBClg52rYx4R5iZLJVpkZfFrIbxUI+b6yLdRsrzRJ9mZqql6gZoAsNDAWtYtsDC3ifw+uRi38YiYr0H9S7Fk+Spk+v049dgDMLS8v1Kl5MQj943525svP12xsZCne5PRI7HdlpsoIpFYsXqdEineY6ctFZuKvppLPIrA3WcnzJq7CE3NLQiHw4pX/bB9d1GsLenw9CsfYtftNsPZ/zoUI4YOxClH749dtt885jGPPv8ujjhwV/zryH2Vx1A1mv122xaPvfBOjDAnn/keO24ZGRPd0hjXrKtSRHUy/poxV1lcXH3hv5REzkP33Vmp1CLIzsrEbVedpXjWqdLNlpPGYOK4DTF/sbowePjZtxVLDz2Gjg+Nce9dtlZ+JhYuXYmvf5yK/1x3npLsusmYkbj7xgvQ3Gx+QcMwKUXM9UmQTjN8vNoi3g4xTOKXLAckwEWkXC+EKXIbsWy4IPnTsnKJJoR5RIC6ycoiaV4oUp2ZFbuToveZd1bHXOv8GZZhZRHHhSgrd66OeW76EXOP9j6Smfy5a9HG8JVthk8ytfdKEgJhCRFzj3pbr00p8Wcj7e53v4h5j/CYG2344wTbbzlBKTGo55V3PsdltzyIG+9+CuedfDiuueAk/PrnTNzxwAt48LZLIo+jaDhVBhnYvxQNjU345qc/cdPdTym/W7NuvZKIec2FJykVTt786BtMvuG+hGMg0UvWDr0IJ7FOVo50Kp0QFO2//JaHcNk5x+Hyc45XEizvf/J1XHxmtDLL/775Fbfc/TTO+tehuPmKM7B85VpMvvH+mPHU1jcqSa6lxYWKf1uMm3YVuvKXk+3lgmvvUSraHH/o3vjx97/xf4+9onjKiWAwpJRwfODWS1BaUoj1NXVKxJzmT9h9Djv9aqX6DJVMJH/5kuVr8MHnP0ReY/KN9+HuGy/E20/fgcqqGtz18Eu44rzStOaOYVIW5muN5ZZYwqhJ0e+tFuZiMaKIr3BHTzdVBxG+blsbDHmc8ZinWh/bCvsI7ZSIMpCmrSxeuTYWEqKifKaImNMioLOIvsyxxAjzwdGFU5vdEfN8eVYWiR5z9flSEcYetITUKHeW6Yi5evvoGg+uKBf+8u4nynuMMHc7JJSTiWWCbBX0peftj7+JfH/dXYlLIArue/J15asrZs1djEGTDoy5jyqQ0Fc8+sdRtZj4v6NEzfj7Xnv/S+VLD1VV0UMe7mdf/7jTvHHydscL7sGbHYxU+OybX5Wv+AUQQXaUc6++u9O/p5KRZHFJBlW+OenCW5IeK4aRSp6uF0C+GxJAPXHC3OIodbxdIUHE3GNlwxg9xQOADSaoVVAoYivElL5kodUecxNWFmnCXETLafeCPN1ORszj8w4IUTKxhcRp2J6xxAvzihUmkz8llUvsbKcg5QZDNlvCdD5zEuaZaUbMf2/w4MP1YRxYDHxZw8KcYRiGkQVtiwvLiKj84DTU8lx4vq1opJO0hnlyYW5JYmGiY3HMFUDfUvV/Xvh3VJSL17fSY24qYi55kWDWX66MRfIxSrRgq1rV8VyxuiqL3mNOOyjtbc7WMaeIuc8dVVk84TA+ql2g+MvPyB+GBm9qJROzTGppIehJ4B8334uNsoG/m1iYMwzDMLIQVon4et5OEl+FxeoodSTBUle7PL76hhWl+OLZ+UhVlBNb7gVUruxoY7HUY24wqVDXyVF6xNyojYUIy46Yi4RgnQhftRD4+Clg7dLO/9YqKwtRWOZsHXN6P5itRSA5Yt4nHMJ+bepu0ikFw7t8fGuatcyzPNHnaQ558LfJPktugJM/GYZh3O4vd0vy58hJURFEWL3tLcrR6cUgiR5hpbAjYk5NZDbfPRotpgXSVvskrqlulcc8reRPFwhz6R5z7b3QENtsCtO/T0GYW2BlET53ge3Jn+l7zD0Rj7lPag1z2rdpSSEBsy3N7p9Z3tguot0ZFuYMwzBuLpUoRKjTTYZIDA0Ypgqb+X/aJMyTCFJRoUUR5qLduwUbwD4/sN+pqu3h7++An7V8nOHjtHHER8wt8pgb7ShpRR1zszXMrbSyiBKaBojkJMjoCyDOzzVxSdmpLqC0CjFpz0u2xKoskqwsBfrmQinMtcyIeXen2wnz7ukYYhhr4fdFD46Yr12m3uYVIOxQYpbCyInq7coFUQuH1VaWpMK8NlqVRe/xlt2EafM9gJJBamT261eBad/EjqWDMLfAVkMLjgx/8s6fybDMymLCIxC0qCqL3sqSKlbUMV85P72IeTp+d/rbnLy0kz+jVhY5wjxfWwB1VcNcwBHzKIanYOvNxuH5+6/Hn58/h1XTPox0TewMqhv9v1fvw+Lf38FPHzweU1vaKKFwWOnQyDCMCr0f6H3B9NCIOfmZA23qB7AQJE7Qb7B6u3yuPQmXnQlzEaGsWB4rzGWPZ6Dmjf3jf6ogpXH8pavC1MFjbsG86BMMTUTMw7J2EtKysthQlcXwWCR6zFcsiL0/5XKJEhYsQpSbPTbxVhZJx0hEzOtTXHSkHTH39mJhTg1sZs1bjGu0tvFdQU1xXnzwRvz0x3TsefSFSmk+qitNDXPM8O6nP+C8Uw5lcc4wmiin9wO9L5geGjGnqGydumUfdtLOUqAlP9ZWWpfkmFSYx0Vpv3gJeOQSYNmc6FisiOALywT9z4Ip/1NLBtrlMc/WLU6MLMBDLoqYayJNyvEhoScWrWaEuayqLLQ7I87P1Quj5wSdj6I6S1fIeB9FaphTN52Qazp/FmjPV5fi/9auDT0zzXKJorpLd8bw1eObn6YqX6nyryP3wbKVa3HLPc8oP1PHyq02Hat0ffzul7+MvjyWrlyL19//Gqcft7/SeMeNx4DOjyx/BlrbA64cn1PwvMidE/pbipTT+4HeF0wPFeZUko+EefEAhMnnXe/QsRblGuuqoh0OrRbmmUki5iRAhFi2MmKeKDJL35PXfPz2wNLZsY+3ogupmVKJhBCK6cwJLQT7DQUWTkvTYy5xIUcVSEhU0zlgpgW9rLGI9wDR1ABUrwVKy002gUonYi785WnYWHRjkeUxz9MWCfUpCv1oxDxsypwZiZj3AHFhebnEzSeMVrpA6vn2lz9x82VnJP0bn8ejiO5krF5TiUefi21c4yb8Ph/65edjXX092kUEheF5sXBOMmUnm7lgXvS3vZH2Pn2VxZqvuQHhhmrQx5yXxPEqZ+alTYuY+xuqES4eoFRb8Ph8lo6lTROD/kCr8lqJyPB4IGpi+DP88JDtRwI09+1axNzfVBf7+r98qH4Ruvtpea0cpww/MiTNSygnT5lrsrEYeZ97EAZdUTy+DNPHqP2AMxEeNgYeWoD4s9Tzsa0ZPoPPF5QwFkE4r0A93i1NyDQlaMVYfGldN8M5fdRxBAPwI4Rg5UqEFGHemvLz0nGl88XnyzA8p4JQXoH6XmxpVOa2vk3LRzBAY3tGJGLeGvYbfo5E/+8bffrh7bwyZCKEzBTEeUAR8mHk+ryGjwuFaDM8mnXLQ8fV49rPobYUPuctF+ZlpUVKt0Q99HNBfh6yszLR0trxIpqfnY2iXG113o0hwcV0hOelIzwndEH1KJEw8QHR2+dlRX6h8sFf5g2hua0RZJrILu7vyLyE/NlYqonkQZ4AWnJyQHH7zMwsDCrUNRySCInAJVq0eEBWJjI6eZ3F9KHu8WJAUTEymo0Lk0QEs3KxjNrQ0/+cEYY3hf+z2u8Hfdrl5eSgVNK8NBaXgnpaZgXbDc11Y6Zf+Tt/ZqbpY7S8qEwRfeFhYyP3lfiAPIPPV5+dBdrfyM7MwoA056WlbABWk3hpa0a5ieeqy8xEFc1nZhb6pzGWtqJ+oGr23rYWDC4sRHVDlXLs/cG2lMdVkeED1VEpyM1Docmx1Jf0U+c20KrM7ZQ1pp4GnnL1ulvZ2gdT1gww9LflEk51TwbNXhv698lFeTCuPnwXZCtR9grl+5L8vsgOy7W0ybzeLq6is68bNhiqb2lBU5ucqIcTcGQ4MTwvHeE5idJ+wnUI5+bD/9Q1SgJQb5+X9izV01u5dhXCfvX7pmz1A8LueQlRu3FlAPVYXbkOofx+yo9toTBW1sTVkpZE2J8V2eJfU7kGniS+XSWaRfYEnxdr6hvgia9tbZJQabTN+eoqnce8E4JNqs2jsT2IVknzEhykWgLamhoMzbWnUR1Leximj1HAr9k1yDakNVhaX7UONQafL9iglvFrCYbSPl9CJep8BJvqzT1Xs+qRbw0G0xpLKFedj1BLk/I8oaVzgS32RaC+OuXnDbSoybx1bW1oNDmWYFiN5rbWqa978GgDCcK6iPmv2vWkKK8dWwwwpu7fn5N+zl9dqbrv1dRM86mViE2RQl/Uv7K4phbtYXkRcyc+hywX5hWV1SgriV1O0c919Y0Jo+VEMBxGsAd8GNOBTGXborfB89KRXj8n5PccNFL5tj0nHx6t02OvnRfy0GpNQwL1NUCNKgxDmrXC9nkRSZB1VerrBlT/ctjrtW4cOVnqbSiI9pbOfbu0y0LVR1RrQVCux7+hOvX/UWs0E/J45M0LLVBorluaDD1nhmbpoRKbpsciGjy9fDswaRegeCACy+cbn2NxvngknC/amMLNDaaeyxc5Rt70dIa2m0L5D8o45v8FfPEiwkvnpD4uLQ8gCI/5sWg7WaGmeuV18zPjmh2liNip9Pq8hp+jLdhxl+qcpnXYsb0Br2QX46OsrkPqzaLsPkJoMzgVHm9UmDcGFKMSZGL39dZyYT51+hzstsMWMffttM2myv0MwzAKok6zSKoyX/WrZyA6+VEkuKVBTbhUqrI41P1TVIMRCZcyEgtldruMtHyXuIVtpiRfpNmRzwXJn2lWZaH3oZhPqj7z3VswjcykWFEdhqqQmBqLpORPsWgRFYOoYs6ULwyORWLyZzo1zPVVWSS9p7drb8SxrdWYou32dUVbGuUSs7S/aQn1jK4epsoljtt4hPJFDCnvr3xfPqBM+fnqC/6F+2+dHHn8C29+hmGDB+C6i0/GqOGDcdJR++HAPXfAEy+9L/P/YBimO6NFBTtUO+itiGgtVVqgD3zR4TCnD0IZWqTOTjQbg1gg2FIu0YAwFzWYIanUm0IfLcpnxBpjRR3zrGxj3STjxmK6KZUQnrQIi28573RVFoIWrGaQ1fnT7IIpZizh9Es3Rsolmu/6qSDqmEvv/JliHfM0Ggxl9aAa5oThIzBx3Ci8/dQdkZ9vvux05fb1D77C5BvuQ7+yYpQPVEU6sXzVWpx4wc3K40477iCsXluJy2550FSpRIZheigszGMRdZpF63kSZfSVlYNAXiFQSWl9DtUwJ+xoMBQfkeyMkEsi5lbUMTeyc6BHHCOz0Xvxumbqlicbi5SIeW5aEfNIcnnaEXOTCyY9MsYSKZeYnjCXXcc8PyLMfdZHzL09p1QiYfjq8cuUmRg06cCkvydxnuhv9jrmYuOjYximdyD8mvEi3Qkokrb/GcCqhcCfXzlfw1xA0eqywQiKSK5TNcxlCy0JwscjGtjI6nJp2spiwbwkq+We8lgy0rOMpLIwsjNini0i5matLGE5xyiycJQhzNMYS66cOuaedHdY4igwWMe8LZ2IuadnRcwtvKoyDMOYEOZOR8z7DwM22QHY8TDnI+bxwpzy6JwQ5hGPuZ1WFgPCx4qIuUh4bTAhzGV6zMU82O0xT6ehUDwyd1hEC3qzEWKxiEu386fZnQzpnT/lRMzFMQp6ZVtZjEXM/RwxZ2HOML2aoaOBXY+xvoNjV2TorSwOR8yFEKIETH1SqhMRc2FlIaj7JwnzPE0w2gVFoUX02E4rSyRS3HXE1hNJ/vS5JGLuvMfck7aVxYCVyM6Fk4jkm46Yy+r8KcNjnu68eKI7CNKsLBlSrSz1NnjMMzlizjBMj2HnI4Bt9gNGjHdRxNxYcwnpiEiYXpw57TEntARQ2yPmohJMe2t0u9wOYZ4thE9L6iI03SiogP4vcQyc9pibrsoSkBQxT0N4WhIxT1OIylokSImYp3ne0jES/4fLPOYF2vPVeW30mIfQI3BlgyGmB0HRNvEBwbgPEW3J6+ui5M8sFwnzYqDa5kTLZB5zLQlPabzjROKn8JfbHTFPJflQtrWGRDmJJbp2GSlDZ4XHPCIADTaOSfcYSY2YB11ULrEHWVnEIoXGkO7nrGSPed+yTZWoea1BK0tWGhFz8RzdHY6YM9ZB0caLHgb2OcXpkTDJEH5ucYF3RfKnwx5zvcedhLlbIubaB6+sD07T/nKrvNRpeKsj5RJlCWKxU9JQSzPusJXFqaosImLuouRPSsxO1/uu2UeoOZZrIuZmxyLLX657D8mysgQ8HlR7M5RmW6kgot2ZHuPqOktrMNRTIuYszBnr6DdUvXgNG+P0SBjXC3MXRcz1VhqnhHmiiLkQ5jIrjxipYS785cpY7KjKYqbBkCRB3KfYeOKnVQsWKQ2GTPgDhABulZn8KaESioh0O+0xz5LgMU93LKIRmRRhLrfBkFEiEXNPGlVZOGLOMF0gGqGICzzjPtwizDNc6jEvKHKPMA/ZFDGn59/5SKB8VFypxMoETVq81nXayzKQ/Ck7Up1faNxfbkVHVHoeXet3U902zS4UpEbMJXnvRUUWmgvxnE5ZWcyWsdQT8z5yNmIuzpeghIj5sGArXq5dhLsaVqT8N5GIudf46/U0jzkLc8Y6REULcYFn3AWJYRHBclqYZ7rYY+7E7oGYgwRWFssr6IycCGx3IHDExepcdOYxt9LO4mjE3ESpRCu87vpzMYUk2Nix6I6RmfFEIuYykj8lWY3SrWEupRKKG60s6dUw11tZZCz8B4XacVxrNQ5rTb1rLkfMo7AwZ6yPgtLWu95DzLgDvQB2U8S8t3vMhb+cqqDQV5w1wfKIuUgEpqj9NvtHI+Z6j7mwSVhpZzEgfKIRc8ke8/rUhYWC7Mi9OBfbWo1HiINpLp4iwlxCxDwsaV7STfyUtXgi37Q4Nm5I/pRpZZEQMY9UZDGwE9Aa8kiImHvQE2BhzliH39/xgsq4B71lxGlh7iaPudPlEhPVMLfTY65/r265jy75szJJNDbDRQ2GZEfM1RKVjnnM0/ExpxsxtyT50yvHytLSkP5Y0rGyxOxkuCFiLsPKIq9cYr7Brp9EezrlEj3qLUfMGcZIFJTtLO7DTRFzN3X+1C9YqGa43clQJQM7WkfsrMoixI84LrQQIDGj91sLcWNlxNxIgyGrqrIY9ZjLjpin0fbdk663O8uFnT8lRMyl7K6IczPQll6ZwnQtWDla8qeRkp42JH8a7fpJsMc8Cgtzxjr0XRM5Yu4+XBUxd5Ew10fDnIiaUzUjYu3S2PuFALCyRKH+vTr/z+h9JFCFiFAIy49S66HnFAtHRzzmheasLDGVUCSQZuWPtLp/yrSyyKriI65T6UTMZZwrZmvLy47eW2Fl8fqNFAjtVJin2vUzbY+5V3sOFuYMYyBizpVZEhKcsBNW738OwsJXbCd6AUxRUScFsd7K4nebMC92SJgvi70/UpXFJivLohnA7N86VmSJG48lwtxg0qNUjzmdi+J6ZbZcojSPeZoJhmbHQ9duYZmSkfwpq4qPVI95GuOQ1XzJTVYWXU5COE07i6mIOVtZInDnT8Y6OGLeOWO2RnCfU0CXMN+wccCMH+19/XgvN13kjVZ+kEVGrMfc0eurEEP0YUdzYnfEvL8mzNfFCXO7kj/Fe5UsDF+9okZbp3/f8XGy7SPpJD3KjJiL402C1GwlFGke8/QSDGnBEjYzL2JhQsdYxjUhxu/uNV/qMBIxT0eYi3KJ6UTMJSR+urSOuTIsjw9ekbCbTvKngf9LRLuzzHT+7GFWFhbmjE3CnCPmMQzeCDjgzMRzZRfx9cLJr6hP8HPKykKROrub6CT60K1cCQzZ2N6IOSVakvgg2wq9fsLkT5s85hSVpIjxOw90HmGz4lgZ6PoZ6zH3OVcq0cqIuUlxHJkXo+eMrIhwsvKaZoW5ftHoZLlEs02fXBwxjxHmtCsXajP9XFf1GYzb8gYiaGB3hCPmUdjKwthkZeGIeYSi/mqNaL0Yd6LbWqKIuVPEl9PUW1vshI6DOG+FMC4ott/GUrmqY1JZpI65TVaWrsRPWFKjljSbC0m3sphN/LTSY56ulcXo4indtvcdxqFPFvbJWTSmO5Z0zpU0knKlLRJoV0kcVwnJn/pjlG5lloDHg2pvhm0R86weFjFnYc5YB1dlScz2B6sieNVCeESCnRMR4nhPuaPCPE6IO+V313ubq1art3ZaWZLZWOyMmEeEeRdROLPRWCsEqUwri6iKY0aYp1uiULIwN90RVWapRJnzEmkwlE65RP2C0uNccyFlLMJ7b2JOxPW6vU2tDpMmHjI9ae8jGZVZjMIR8ygszBnrYI95YvpqnRR/+xQe8cHnhDCPT7J0UcQ87JQw13ubha3HTitL/2GJK7LYVS6RnluIjq6ikrKjw2lEJKWUS6RzcK8T1YUzsW658ecQCbGy5iVNy4RpYS6z6ycR1immdHZYZEbM0zlf0qyWEx1LGjs9olSijMRPDa92/qbbZOj6xlV4pH4pxgeaDUfMM73aIsEAWd5wj4qYs8ecsQ6uypIYIYBp+1G2J9WMlzp+XE4nf4oIekDSNrrZD9z69fYL82QVWQjduRKmzoNWoH+fdmUjsfLcNWhlSXssVB7x+GuA4gHqz1O/VL/SEX20k2C2xHXJIDWYUdQvPa+3WSuLbI+5WFjSONKKmEtoMKQv++k16XcXC/gWB5M/c4W/XIKNRSfMQ75M1WOeBoe31mBioBnvZBVhJuJymZKgj3b7PdHyib3RysLCnLGn8ydbWTp2dqQLql3dHDuLmAfa1d0NN0TMKRJGUTH64JMUrDNdnq5ufVS0UZQvpo63BdB7RAixTqws0Q/yNBqbdGUVoOOgj3I6JsxTS3r0pGtlmbCTKsppMfbRk8CSWWmXnDM9ll2OArY9IPY+k5YST9AlVhZxvijC3GSUmhbr4jqZVsQ8rkKMDcnJ1kTMJZZK1BCVWNL1mOeHzNcxF0K7LWjcymJEzLsZtrIw1uFjK0tHPHER84DzHnPRYdIpYU4Xb2F7aqp1NvlTHzFvqlPFMH1o9ulr/Wv3G6LekoUmUdKdXvRZdb5EWp6nIHysFOYGun6qY0m3g6J27lPJUrOiXFbjpUEj1VtaJKxeDPzzm1pT3hEri0xhLukYBdL0VMu0sshK/jRj74l8jsgT5pEmQ2laWczUMdc3B8o0uCEYiZiHLdpJtBmOmDP2+IbZyhKdB/FhQJGOSMk5B60sJAQpWii2Rp08TxrrlG18xzzm+g9cihg31Kg5AWRnMZMMaCbxM5GNJd6/bJUwN1KOTmYlFGLoaKBihfq+MOhxTrsqS2RBJqlmN40j3drUHz4BLJ2d5lhcUi5RxkJORnMhQr/zZTYynG7jJxmLFeExT8fWkyxins5iOxxGgTbH9QbmNwQPAmEgw2O8MosQ8tz5k2G6gsslJv/QJeFDH1R2lcDrzMoikhyFjcH2cWjRcbqYi21Zx5I/4z5w7fSZ99MSP9clSPwkaKFg1jNshYdXZu3w4eNUj/d+p8cef7uqssgSWjKaDOXI8w6nHTGXamVJM0HXyG5OJ3hkjCU7p2daWSLJn+bf09QeLlNL3jQSMdd7xDPNRsxZmDOMgaosWTkIW1HvuLsRl0kf+eB0ImKeGSfMcxyOmFPZLxGxjK+xbhfxkdO6avtqmXcVMSciCzmLzpdI1Ysme60s5aOiAl2pDGMsYpt2VRZZVTZkVKsRi3cJFgXT15dsN0bMJXT9lNVkyE0Rcxk1zDU8kYi5+YV/vq5jaIPBz3zhEc8yKsy5XCLDpEh8N0t9jejeSm7cxdTJiLlbhHlGR2Eeji/laBdZySLmFtcypw/m0vLkpRLt6LZptE60TGFeOji6ICOvvVGhnG6bdYPJppbNCy1IxN/JiIRGxmKyKoslEXOzOwmSrCwykoWNVg1KRjoLSksj5uavLwXa/0SiPGSwepS+ZKIRMntYxJw95ow9VpZIFKYdvZpkwtznAmFOHzZmy4elNQ4tOt7eqouYO13H3EZhXlAC7HqMupAlISSORyKsruJjpOOjzN2e0kHR7wdvaDi5zpN2BNSgdcYqYS7EFr0Pgu3ORcwjwlxiydJ08wBkNBeKjCWNpEupyZ9pdM+1RJin7zFf7MtEaelE5JqoYmW2yVBWD4uYszBnrIFWyiJiThn0GZkIk3+1pQa9moiVRQhzJ60sWVHxSR9U9IFJF/tGrTKK3TXMSZjTl35sdhMfORUJn1Z5zDffE9j1KNVnTx9kP73X+eOtruIT+bC3MfmTRInotkmUb2Q8+TCS5JiRZiMfiRFzM+9pkYAtyZ4QtfhkONtgSMYOi8yIeSiouqDNnLs0fpEXk+754rY65hLKJVKPhSpPBrRaX+asLN7e7TFnYc5YXyqxoRYoLFM/bHu7MI988DbECC3b65jTB4HY0aAPX4qMUTTfCWEuPOa0gBMfdH6XRMzFXORZUC6R6qPvebwqTJfNAb58uXMbix0ec0NVWSQlfxb1jxXUFDEX54ThOuZmI6Bxx92xiLnkbo5iIWe2jrklHnOvc82FBKJGv5ljpN/Na+1ZyZ8eYWVxwlqZTvKnp2dFzNljzlhfAo9KzhFcMjHaXIhqZFvd1rwz9BFpEsPi4u6EzzxB8mfY8Yh5c7T5klXHZ8AIVZRTicCXb+9alNviMTcgzGWdu8JbT/NAQoUSbcV5mHLypyyPucPJn8LqJikKasrKQueWeE+6yWMuq1yifiFnxkIizhXa3UvX9md2TiioIqL2Te6KmG/T3oCH65fhtOZOLHlJ4Ii5CgtzxhqEjYUifNqHTJi7f3aMcjjVYEhs3ZPwpA+XiDDXhIGd+BN4zB2LmMcJNBGhjk9klsHA4eotNZFJEY/VOQkR8WNj8mdZeXQe4hcnqQrldJLo6PwTAs0tHnNJUdColcVnXHiSeHWjlUWG7z2dSLXMRZzZ81acJ3Q9kGG/iveYpyHMJwSacW5zBfZvNb7zKpI//QYi5j6E4eOIOcOkQMRf3h6NunAt8w7Jn1GhZXfEXGzdaxd1RyPmiTzmDldlEZYGK4UwRcyJNUtS/xvLPeZC/NhYLlEkflauBFbMj31+cT5YVa9bf8zpOdLpKCnDY67vCiwDM2MR12lFeEpUOunajbLlLVo86VhZIo19JOwm6EoLGoreW2BjianK4jUfiCjQzrk6E8fZTLnELN3LcMScYTpD+JcVYa5GODhi3knyp92ePmEVcYMwz3CRlSVZxNwSK4sWMV+TesTccuuToQZDkpI/hZUlXpgbiUimI/oiFVkkRR5d5DE3tWCJ+MslRst7UsQ8v1C9ldEJWETMjc5LxPIkV5hH65ibv74UaM9hpOtnB4+5N9yrhTknfzIWC/O2qE+UPeYdyyU61WAovgqFyzzmjllZ7IqYUzIpJX/SB/O6ThoK2VkukXYuxPPalfxJUcLigVFhLhYeBoWhKcuGFc2FZHnMpVVlMZGTYEXXTyndNmVGzNPwmPfRKjQ1yBDmuvOd5iXodMQ8fStLvja3Rrt+mo6Ye9TbYJimz2DWqEvhiDljDfpSiRErSy8X5vThKERAs8MNhvT2EbdYWfRVWRyJmHs6+ket8pgP1GwsVavUBUmqWGmtEceedrlSGZMMK0tRP3Vu21qB2ipV7NRUpBExN1NlQ6Jn2GUec1NjsaIii9mxCPoUqdcEOs4yFi2hNKwsboiYWyXMwxIaDGkR87q0IubotYmfBAtzxhrYypJ8m5o+oMRixeqGMamWh3ODMFeSP7WFgj9Lprs1NfSLAWFrEEJYtn0kYmMx4C+3xGPuMddcSNZuj7Cx0AJFHPGVCwxHsKMRc6/zEfO06pjLrspior67FV0/07WPbLBJNEE4xbyDVCwbpsZCiwRpEXO9MDfhMZdYkUW/wyLHymLGY65ej8xEzHtK4ifBwpyxBk7+TK15iFPJn8Iq0uomKwslf2rj8WU4sFjJiR4T0XVRL8xljsesMJfZkIrqh5/9X+CoS+M6KxoU5uksWPSJn4Llc9RbI/X0ZSR/yqpuISNiLvocODGWSHMhi6wsZmwSIzRhvmi63LGY8pgXyYuY02LUjK3Gsoh5+laWSPInR8xNwx5zxnrfMEfMkyd2mfGAykAkvLnCyqL3mEejYWHREdQuEnV/jBfmEqJ16QhzquKjdixM83whf/sxV6iNv8hOQp53o3WiZXjMSwd3FObTfwSy8oB5U+2JmEeSP522snh0gkuyx9xNVhaji0rqIj1inPr9ohlyxpKO9UlmxJygc9fnNZn8WW+NxzyN68vRfTdAUTiodP+002Pe2oMi5izMGWs7f7LHPEFil9ZcyNEGQy6ysggBTsKcPjBJ/PqzENI3qbKDRPWJaTz0RdEsWYsnEsH5xerzptJUSI+MHRZ6Hx59uSrKBeUbGq96EcmP8MqNmNNuxa8fGXueiDXBBR5zs+/p7Jzo38iuymKoXKLVVhaD8zJwA/W6RAvGVYukDCWykDNst/AAffpKjJhr80LXFq+LyiWm4TGv8WagxqS0FOI6s5dHzNnKwljcZr09GnnJyrXfN+xKYd7QoQWyYxFzEaGOCPM8Z60sujGFhffcLpJFTmUnXA4Ypt5WrTEegY+ImzTGcuiFQL8hqrBYME29b/AoY10/ZUTMKRJaoqvIkgZyPOaSrSxGF09iR43OP/1Ojd0Rc7dVZRH+8iWzopHudDFbXpOu4XQdoL9v1AVY7J4Xi4S5RyR/2h0o0mjXpiKrl0fMWZgzNlRl0T7oM/wIi0h6byS+hrmTVVmSRczpgmy35Si+QowmkEJ2C/NkSYAB2cJ8hPH65bIWCbQNP3ysKh5fvxv457doxNxI1890o9REIVVkyVSPe43x9t2xY5FQLtFpK4sVtanNjKWgVK5VI34sRr3HG0yQ6y/Xl0s0eoyEv5xEub7UYTqYWeBKrncvs1zi3fXLcXvDShSJoJOZiLmnd0fM2crCWJ/8SUKL3vBeH0LiQ7A3kqhGseOdP1uj9gH6niqTUDRGtr80pd0VrUSflgBqf8Q8iUCTvathNvEzpoqP2SYtukhbxXIg0Bod0/o1Bq0saTYYKu6v3iqvm164K6065vGLVKeEuWR/uek65sUD1FtxPsjCTGSYFotkZSEWz5A/FqNWFqmJn3ELXFclf5q81oXDmNy8Ton43p/Tz/Cft5lI/sz0xv5tT4Aj5oz15RJ126Kh3pwAmjBi7nDyp3773imfeSRi3ubOiHnQfcLc9Fhy4hI8q9ep0T9aSA8fZ9LKYnIsIkIvoQKJKctGhwVZi7MecytK4EXKJfpSt7HkFVgkzE3MC52TJOQrVkgVw6atT5Q0LXs3weiiUtnVzLHGypJm8mdeOBQRlfUmFuymIuYe9Y/YysIwRjp/EloENiQ+BHt1uUR9VRaL6mSbEeaRXIAcZ86VOI+5Y8K8g8dcYolCeo2CEvV7o4mf+rGYFsOiJKLuHFw5X70tKDYozNNM/pRZ/SMtj7lFEXOzHnOJYsvwgkVEy+vXy6tAlE4dc+Evl1WNRZCulaW+Rt5YjJ67YgFHf5fqe9VogyGTn0Wi6ycd6SYT8lJEvbM4+ZNhLPaY6yPmvVqYd1KVxe6oeSJhHvG7+1zhMQ8LwW4XySKnoqa5jPwI4d+lqKiJZMNosrDJY5QosW+FJswFRsslmh2LRG+3x01VWVzkMY8K8wxjwpwSk2Vjxm40ZGP1dslMqUOJesy9zpZKNLNgEcKcRHk47KqqLDFdPym525aIOWL+tifAwpyxycqiftj3ao95ooiYVQ1s0hHmdttq9HXMlVunIuZJIqcy56WvFi2vNZnsmLaVJYE3NV6Y66PpViTzuS5i7hJhboGVxWN0t6d4oDU2FrPzIuakpkLyWNzkMTe4YLGohrmM5M90un7GRszDJiLmxhcCboWFOWMNfl3yZ4zHvBcL80TJn6JOdm8V5nQBj99dERFzxzzmLdZZWfqWOSvME5VEJK+7foGYapm8dFrPxwjiJnkRUDOCQna5xHQ95jIFl1kry3o3RMw9lpVuNF2VxRKPucHkT+qDIDsXIW7nyayVJSZibgLRYCiTI+YMYwFi219EQTUhEOytVhb68BdiSvfB64mJatkoiIXo1YsRJ2qq65sIOV0uMZmlQWZJSxExr6sy9/fpniuJmgiRVUefiJqqlSLd5liRiLmESLWMzp9Oe8wT9Dmw32MuIuarIR2jlg3awRJiVdZuRrrni6Uec5+xxYHMMcRFzMMmrSzCY25WmAtxndXLPeZcLpGxVnAJf25rL4+YCxsLiU6xiyBQLoZ+G73dnsRRQicWCMLyRBd0MS9OR8ytbDAkPOZmI+ba4ils9lyJ1CqP85GTnaV8lLHoZLp1zMV8S4iGmupwKcYuzjPZVhaj4iRR1SZZY0lpXjxxJSwlYzRKLRZuFNwRnyOSiO6wGFCA9P7PLbCwKovXuah9XPJnMC4IEfRk4OtNL0Nx/RJsNv81JaCUiP9lFmBk8Xh4TJY/bVPsKGFTdcxFtL0nwBFzxuJKG7ER815bLjFSkaVeTq3hdKBa5QK9MBfC2M6a6mIs4jyJ8Zg7lPyZzGOeIeH4FJY6bGVJUJVFX5mFRHKq3RXTiVJbFjH3mYuWu6Hzp7hGOFWVhaLBtEihc0y2p9tMxFzYWKzoqWDGyiIEMVnuLGkCZVSYy4+YR8olxi0qqwpGYHm/LfD3yCMwd8heSf++xePFoowsLMzQva8M0GYiYp6piXiuY84wXcFVWWIRkZYEF3TT0T6ziAghiRlxfJSfhejzO7eAi7GymLu4m0aINCutLGlHzCVZWeIj5ktnA7VVxpq4pG1lkekxNzkWMQZaDMrq5GhqXshPbUHyZ6RyTgrnS4lmY6lZl/rizMp5ybLGX266jrmoyCLbQmJ0kWChME9WLrE9IxpQ+3XMKagsGAkrEOLazx5zhrG48yfR26uyRCou6EolOpV02WWSowNed33N5IiVJbNnWVloESKat9SZE+aedDvFJkr+VH5uAh69BHjv4dSfK52mPlZFzI3aEyK7JJKi5cpYTCy0KTosRKIl0Vifs4mfpiLmedZFzM3UVM+3SBAbTf600soSiZjHXuvadRHwkNePrze9FC3+jo3oDmytwW0NK7F7W4LPOSMec4/xzp/sMWcYo+USNeFle23q+K1amWWuumLwRsDhF6rJXOLi21nE3C6PeUQMxwvzNEWfqbHENaLSR8wzXNL5U1ZSbN/SqAg2GwVMu8FQEmFOGK2JbEbcWF2VRRmPDwiGnCmVaNZjLhI/6byQFbk3apOzsoa5GbtRpCKL3CY6pquy5GsNuGR/hphN/myotTD5M3YsbVrEvF/1XDRnFqA+byD+2PhE7Djz0ZjH7dVWh/ObKxCAB19lakEIExHzzF6e/MkRc8aezp+aoDCdtJYuo7cEzr8fOPgcU40PTLHRZqqFpXQQ0G9I0miL/R7zBKUSnSqX2GnEPMveBYL4v63aSYhUZDFpY0n3GFFETogdGVHZiKDIMJmAnC2/jrkyHm/69iW7I+ZWlEo0uui3OmJu1G4ks859skWokd0VqyLVRmw19LkqFtcWRMw9Sa0s6gI2t6UK2/zzjPL9muJxycslmlysm4mYZ/VAK4upT5qTj94P55x0GMpKijB73mJcd9fjmDYzrkmFjtOPPwgnHbkvBg0oQ3VNHT768mfc8cDzaG2Tm2nNuNDKIrzDmqAIezOSZnRbHr0mxm6ripLPX7T+NcWFfPr3QPVaIK8QmPplh4fZXi4xIsxbrfNSm20uFJf8adsybuiYaCvyeJEWSYrNkOQvN1kqMV1hLkS5LN+u2Ekw1dRHbim8iABVxuNLf5fEbo95ouZjMjAlzC0olaiMxaCv26Ia5rELli7GUlCiBnIoJ8SK5kIxCxZv6p8pdI2UXUJSFzGPr8rS7lM/MzIDTSitXah8X5/TD+2+LPiD0c+RgpCkOube1P9GNCPq1RHzg/baATdeejruefxV7H3sxYowf+WRW1BSpBW9j+PQfXfGNReehHsefw07H3YuLr35QeU5rrrgXzLGz7g9Yi7KXAlhbqdNIlFjF2LzPYHtDrT+NcVFdPEs4OcPgS9eTFy/2o764fTBP3EX9bgkq9vsuoh5tr07KsTcqR1/J9vKYjbxUzcWU++jSCtvA5VXOh2LSCw009QnN7qjpm9uZJZ4K4vh2vUOe8xzrRHmKSeW07ktrpGWe8ydT/5MyT5CC8eTbwbOvAvYYBPrki6NWFksTPxUhhBO5jFX3yf+YAty2uuQ3VqrzE9t3uCYxxVo78N6s3XMRedPT++OmBsW5meeeAheeed/eP39rzB/0XJc+e9H0NzSimMP2TPh47eYOBp/TPsH7376HVasWofvfvkL7332PTYdr0UwmZ7d+bNDxNwpYa7ZCOZpwmvnI4FRk6x9zRQvotHojYWCeIdDgP1OBfb6V9dWFhllAaVUZck0WQ3XIHRObriZ+v3cPzr+XpqVRYIwT8dj3pm/3Azp5EZI9nZ7zHrehZ2mzWGPeSQ53CErS2E/dd7oeDTK9y6n5TG3tFyit/PXp2Rt2v09/CKg/7DorppTyZ9WC3Ox8I87dyPCPKC+T4oalim31fmaRVN2509v7/aYG7q6+zMyMGHMKDz0zFuR+8LhMH74bRo2n7Bxwr+Z8vccHLb/Lpg0fkPF7jK0vD9232ELvPXxN0lfx+fxwGc2ocgF+LXohLjtbZD4btcuMv5wEB6fDyGEoLzlfRmOzEubFg3y//AOgq3NCG2yA7wjJyLDSHk4o6+pXUT9zXXKHCSC5iKoXZh9fj98Fs1Ne/+hqsidsAO8yvEAvO1tyNC9XjAcAo3Em+GPuV82wYk7IzRqEjI+egKhrGz1NQPRsYSD7VD2Wbw++LOy4ZEZzUxAaNhYBEgYNdbBv2pBh2MVCAXV+UpzXtr7lirHIKNhvXIMzODV3kceE++jUG6++rctjVLegzQnynva60Om4bH0Uf+2tdnw38YT+V9I+NE5489M+n6LJ5Cdp70XWqWd8yGEDR+jQF6BOo7WRmnjUF5bZ/FR5iVJYmmodJA65vVrLLs+i+uLh45RCq/Rnp2nvF98bS1Sr4v02iL50+v1JZ3vcE6eeh0SAQQtiOBvrk/5/EqF9nBY/T8zur7+BwuK1etlY23Scde3GS9329iuSsEWLT4S8GTEPE+TR13Uh1pblfvzalcCJZtgbc4wDGjzR97DImLenBG9zwhhD81ESImCZ/pIQ3QdOs/20muGEfR4076W2KHl2kRwRZYwLy4qQEaGDxVVsR6ryqoajBoeu6UhoEh5cWEB3nv2Lnjggd+fgeff+AQPPv1m0tfJz85GUW73b0TTL1/bnuxlUP3ppdr3g/rkwZuThbbcXKzURLvd8xLMzMYyLfoyyBNAbUsdKN6Qm5OLskItAiGZUEYmlmpbsYN8YXg7eZ012hu1KL8A+RaNZ1nJQOWCTlGZ0PjtlfvyPCGU6l6vJtMPemfnZls3L3TZXbbT4Qjn5CN/iz2U8pl0LPK8nshYwh4vRIP40r5F8MmMZtL5kJWHYG4+MqvVLfvKTbYHxSnzl81Ead+Olrxqf4Y6xty8mPkyyrKiMuUY9Au1Isvk87RlZyvvI0+GH+UGn6OhpAzUNiYr0IaBEo5va14uVimCIsPwWJqKy7BW8ay2Gf7bZJDYovOrf2Eh/L7UwmdV+X1Bhd3yPWEUSxpHY0421tH/lpmJQSk+55oBw0BneWFrI/pKfO+FdEmxg4pLlAVwImrKR6jv/Yb16GfRe78hJ1s9/zKzUjr/VvfpC1qSF/soUCx3TNXavOTl5CR9T7cVlynvNW9LA7LWLUPz0LEpXc+NsjbDB9oTKMzLQ0EXz7u+pD9oP6NPezNKkjx2ShpOpMXV6mdzO/yYskbLOaBrZFB9rZXrM1G/ZgDqiuqADYDlmaPQvmYAyrWhFK5T5zWnoC/Ks41XZSlURHYlvB5gaGEhgikI83w/7WAE0CenD8oLrSkYIFOzLK7qOsfI8j3rbbcYjwtOOxLX3P4Y/pwxF8OHDMStV5yJtWesx31Pvp7wb+pbWtDUlvgC0h2g1RUdyHX19WhPYXXU0wiLWs3hEFZVVSpvrXBYXX2HfRm2z0tIVERprMPqynUINqrbxU3BEFbWWLMlGKatYaKtFasq1ia9vOijWtUtraizYDzhzGwERYMjSmTUEnMbG+rQqnu9YIMN81I8ACEt0a16yBh4Vi1Sx9JYHxlLOBL99KKipRXBOrljaT/uHIQHbwTfR0/A+8+vaB+mVhdonvFzwv872Kj6fhsDwZj5MgItSMUxqFi5BB6TlgWfT91SDnm9ho9RUGl3DbQ11Eg5vqEMNYpGH56GxzJI/QBvb6pPeyziehuma4ofWNvQCE+KzxnQ3JwNddVolnTOh4rVY9sWDqf8v7UVqSKobukcNEgaB81LWd+oeFtV3wBPEltIIEd9XMva5Za990Pa9aU1lNr1pd2nRqir11egVuKYaF4ytchuY3sg6Xs6lKfusoaaGhB48154dz0Gnib1M0QmAW1HsKalFfVd/J8Bv/r+b6xai5Ykjz14dIupiPk/VaUYVVCpLkZ8HmwxIKrwv8vzKIuHUblrMXTAGlR6ZkHxPJT1Ux73/hzVErZz8WjFzjKnOYCWFuPHrEZL5CRyWmpwbv8g6oLA9SuSS1VPufr5ubqhEStrm3uEljMkzNdX1yEQCCrVWPSUlhSiojJxpvIV556Atz/+Bq+8+7ny85wFS5Gbk43/Xn8+7n/qDcUKE08wHEawBwhaOpCpbFv0OIS/LNAePZlFHXOyudg9L320+rO1Ferraou+kC/DunGIKgsN1V2+oTM0Xx9txVly3oukLvKOTvsW2P5g5cdQS3Ps/69VHyHRZ9m8lEdzS8KDN0ZY66IXamvpOJbMLAQ8XvkXxJJy5Sa476kIku87r6+SdBegJN1EW/2RefGZn5d8qu7gVd4H7Wl0DvQLL77XxLmr7eCQ0JByfNu1jX4z86Il9oZbmuSda9qxa6ePlFSfU0s+DrY0yXvvaceIfLop/W/k/afqH3SqrVma+thTQXc+K0cr2XNrXT+DFSus++wNiDyjFK8vWh5CgPpASB5TphYxD8GTfCyiYlRrM9qpgtX/nld+lD472uvTArfLuc9Vd/SC9euTPjY/03y1uzyfdu56M2KeJ6TlJeWjQbk/s2Wx8nNzTgkyc/2ROZzj8QP0FVLehIZfP6z8ncrv49vh1xzND6wOYGVb4vCWX7G/UOAkhLagNVlJdmsWQ0bu9kAA0/9ZgB22mhC5z+PxYIetJmLq9LkJ/yYnOytmO40QP9PfMr2guZDyfbSyhe3J05Gku6rYSjFWtp43kKRjeblEfX3iXz6KjilpWUAL52WoLheF8khEAq4++TPmGEmeE0o8E23p6bl3PEz9fv6fyRu7yGi8FH8OOlIusecmf6rjMdE0JlkXXDursogdvZoK6SXwYpNiOzlnNGGOKjInWYTR88XC5M9ogyFvCl1h5ZcljMFI0rLoPprG4j6lzp/xdcx9scmfWYEm5DWrSew1fWITQNNBWVRrkCgXOnt8bu9K/jScYfnEi+/huMP2xpEH7oZRIwbjzmvPVSLgr72v1me+/9bJuFpXCvGL73/Hv47cDwfvvSOGDOqPnbaZhMvPPV65P16wMz2shrnezyhKzhF2V2aJr4ZhR/URUfM2he5slnf+1Atz2rl4/1G1Os2c3+wv2zhktHq7dHasMNKXS9QJ87A4l2QhxCl9MC+fF71/7pQUxLA//XMwneZC+rHQh7jRwIarhLkFostMVZZIuUQH65j3G6rerlMrXUgnsvD3JQ8i0LlBn8dWlUokxOd9Sk19qAFVjmWdP1NaxFlRSrPTsaQwL9QLw9JyidGqLGGdAbM9Q42Y+4PR90lhw3LltloT5kWhAG5rWIlLmih7xCwePL/Og1/qgQP+8eI9rQDO2JxwryqXaPgT+IPPf1Rqll9+zvEoKy3CrLmLcPy5N6JyvXqilA8sQ0hnTyEfOdlVrjjvBAzoV6LYYUiU3/mQDQ1eGPdEzPW1iklsxUdIbRHmFXIbxnQG2SOIxhQuoFZ3/ozv6LfsH/XL6Ad4uhSWAQXF6rnwzRvAyTdFfxd/PlgVvY/Ui24E3r4POOYK9QNx8czkfyOjvrso15lOqURCH9Wn8ejfYyl3l2xwTghbGjE3sVCwovNn2GXCXFlwZyYfT8kg9bZmnZya8knHYWBe6PyQ2IAqacS8s0WCFaU009npoc9NsdtncblEZVgeH3yaUI8vl0gUNSzHyrJNUZNP5+/PGBhqxzVNa1DhycA9uf1Nj+G0hdFjslUfDw4vCWNsL4uYm/qkefb1j5WvRBxx+jUxPweDIaW5EH0xvbTrJxF0MmJeFiuKhJgRCwgrrSwpdInzWN3Yp0i7SK7vIpJh9TiGaDaW1YuB1YvUbqhibIEkXUgzrBLm9apAffZGkW6aHClWlrhz0Cz695FRYR7ppCg5Ym7mfLEiYp6KPcGNnT+FlWWtGoGUTlfjKVVzLlBJaX8uiQyLc5V20qxYLISMWFksjpin2vkzpuunBbXdlQVLdOEf8mbAFwwokfP2DPV4+APRuYjUMtci5tEa5vJKXc9sUkpHYHxu74qYd99i4Yx7EUkzwidM0C5KpFOgjQ1sEkUr7ehw2aco5Yh5yt35zJJqq23LhblmY1k2p2OXzbiIuceqiLlIyo1URQkbsI+kMS9acl/6HnN963mD48nuI1mY60JURj+MXRMxd7jzJ421rNziiHkX4ynVIuaVFvrLDUfMLez6GeMx99l7jnZq8fGl9pliUbRc7zFXhqWNJ+CLliDMDESPR2F9rJWlQPvbeomBt9na1I+hDZQk1+qeGDFnYd4T2e4gYNsD3BUxd6rlO11cxRZ+RJjbYGXp09eEx9yC8VCEmPyj9EFEW9VWC9BUEj+Xa4ni83S+7g4ec4vyAHJNdFiUYmWR0PVTcWCGzUeqxTY42XhkoLfVGP0wtiBi7jESkXVLxJwWzbRzR+KPkj+toKvri7CyVFkdMTdgfbKy62fKEXO7rSzeFD9TrBPm8RFzvY2FfucLRT/TCxtXKLctWYVKwCNfW+yY7fqZiAUtquDu4wOGJSxRHka2EOYcMWdcC4nQnY8AdjkKKB/lcPJnu/PCvKA0KsSE+AtYZJMwaWUxXMXBCMUDo5HarmwPVibFUjIs2VboQ2iFlnS5cmG0vXUjtXnRoY01LN1jXmDcZ53ueUvRZPLWy0j+jLE++Uwmf0r2mJsZi1MRczqGexwPDB+n7uwJMeSUx7y/8JdT9NEiZdFV7kjEytLFjpo0AZrhgoh5CosE25I/U7WyWB8xJ1eIR/OZi4h51F/eEtOPwx9sRX6TlrdUVq6zssj7HAuEPZirvTXHaYdDj183II6YM+5FXNCIbQ5wT/InoQkKajJkG4kilVYvEEhMiih9o8PlEoWNRety6VjEXPjL1y7VeTbDwFv3AR892XEbP5IHINvK4kDEnEQ5iTSqUiShzFnU+mRgPFSvWzxelpUlxlbjdVFVlk6EwchJwJZ7A4deEM1voEhf/I6NXRFzqxM/u5oXej+IhnBWlkp0W8RcFKjoTES6LfnTQAnedPAJYa59DrTFlUrUIyqzoGxwRJjXS/SYE7OaVfU9NoHPXPjLCY6YM+5FbL8RG20WjYY4Iszb7C/HF09hXEUW/bisipiLLUd6nRQiPiJCYakwT6UMmpULliFxNhbBmiXAjB/sG4tI/rRTmIsusDW0OAzbn1yoX5DQgkdWRSTh0zU6FifrmIudCxJ+e54ofwxGd8BsEOadLuSEjYUCFzIXJ+l6zCO7OxZFzI0kf7a6LPnTYmHu1cbTMWLe8X1SpPnMUTrYkog5MVs7BcblJveX97SIuc1ZeIytwpzYZn/goyecr2MeE5H12W9lSRgxt0qYG2sCYWnEPNWKLFYLc63luBIxTwFPsF2VsNIj5rqqLKmS7sKpSAhzOa28TS3kZFdk0Z8zNA5D72mqUZ0tX3ilEpEV701i6Ghrqm4YEaCiIotTEfNI4qfF/nKzVVks85incK7YHTHvMvlTCPMU7JFpoPrI8xDwZcYKc10Nc8FGK7/GkIqp+PjnSjyWU4aPM/uiVrIwn6VVZklUy1wI8/YQPaLnNKzkiHlPI/4Db+w20YoQjltZLK7X7RYri7iAplLDnLCywZCRiLmVXvcCzR9Zp3nKu8IqK4u+XGKqpFvfvVC2MDfxPsqWXMM8rW6b2dbUqE5lLKLxF7VYF1gVMe9qLJTvQNcK2nmoUBPpLKGzgIjYUa2y2F9u1Hsf8Zg3WuQxD6feYMg1yZ/Wdv0UZLWp14hWv3qtbPdlJ42YFzStQf+aucp7qMLrx5/+PCzUmhHJYpaozJJLgjXc40slEizMexriYkIRmCWz1A/vrfZxRfInRUFdIcytEn0mk3RMCa3Unhko7m/CymLBvORrFgKR7NnlWKwul2hAoKZbupEaKxGSqm6Y2mHJscgaYGZRKUQXzau+pKqdEfMf341GymVHzGO8976uEz9pN8vKhmudLSxLbIyYRyxYXsetLJFFQqcNhnpf8ieR3a4m4rdkasI8UsPc4gVKEha3AM0hIMcLbJDd80slEizMexqREk8twK+fqN+P38El5RIdiJgLURQTMdeJASvGYqBUoqUec/LT0u4FCe5UyvRZ1RGVPuDEeZlKlRpLq7KI5M+4KjCdke7xERHzaietLKKGeYMLhLlF9aFTiZgLcUNNrn76QE5tebPVaspssLHox0PzQrkGux0DDBhhbw1zo7srVldlMVIu0a465p3Niw1dPztGzAu6tLLYQQieiM98bFxllsweGjFnj3lPQ197VVzwxRvabitL0OFyiTQX8TXM9eUSxQVPdmc5g17AaATUZ1FFlnWxiXqpRG48nmjlAlnRcrJRxOcd2Fm6kc5Lqk4ixtJNrSymxiM8u7JqmAtM1Q63yD+cShRU/9789WN1J2nVQmesLCJokMpulozx0Pkyfntg6/2ATXcD3n8kanO0uiKLfhyGPObWWFmiXWKTHB/6XBCfU1ZbWVJJ/ox0/WyzznevkaVFzFsjEfPkVha7mN3sweZ91A6gH1R7enzEnIV5TyOyym+JFcZ0AdJ/YNjR+TM+Yh6yuVyi+NAhEabfro5vay4bo1uOVjUYEtGwVD/402n3LtPGQljR+VP4y+m5jdgX0llQksAQC2NZVhYz54tYoMr27IrovRsi5l0tWGhRJgSf8t4Mxza5kgUtaEn40QKh0+i9gV4HUo5RRtTaR58TR1wcnQuLvNwJxbAnhYW/xRHzLheU+iIK+nwEK8fSWdJknvXNhQTZbWr+TUvEYx6tY+4Us5JUZmGPOdM9iCSstFgvQA1XZbEwydGQtzcs109NCWXn369uEZuxsphtGNMVG2+p3q5ckNrjrTpfRMKdAQHisSIPwEypRP28dCW0ElHYP/qBmupugSVWFuHZtShibuTctShi3mWbdSGEaUEg21ceTyrXOvG+sFps6RcsImhAwQqxs2CHjcVop1iLq7JEzpVkuysxi0eLVV8qu06R8VgbLVdeql0T5plxVpaA9a+djL8aVQW+bX445nj01Ig5C/Oe7DEPOCXMk1VlsTn5U1QcSNRcJyL8JIxlyGj1Q5a2iKlet1ErS0TcSJwXsk9QOTj6AJr1k4mkNYljiXS9NBAxt8L2ZKZUYoecBJ/JxaEkG4vZ5E8hzJt7sse8i7HYVAc6ZiydnS9CJFscMY/ZYRHvxc9fAOZNNbZwlyrMu5AetkXMfc5WZFHGkoKVxa/7XLeY7DbNyuLvE+cxdy5i/nM90BYChmYBI7M7RszbeljEnK0sPQ197VUSZWJL1ZGIucMe80iNYK0JQsxYaGw5ciLmIqGQ2PukaNv3lKuyWNBxc5Pt1dvFswx88Gs7CXR8ZHq7IwLEhJVFasRcJH4aFKf6BQvNjZEKGhF/uRwbi0Ionaosje6pyiI7+hexBHi7iFBbbB1JZV7IyhHZWau2byzivUgLxXceAAaNUpt82YE4PmIsyaC5idTdtzhi3pWVxeqKLERXOz368VjdBEqpypIsYu6cx7w55MGvDcBOBcCuBWEsbFEVeaZ2+Ei09yQ4Yt6TI+aEiJo7EjFvs6+RTqdVDxIIczEvMoSfiMQqrzlYvSWBm2p0Unq5RE+0Es+MH439qRWLJxGlMxIZ1KLUYZnC3GzEPCbSl2EuYi6pIktseU0jyZ8WecyNlMDrjRHzZGOhxbuS9xMCGg1UCErnGNFiO1/nayeP98r5cktWyrCyiPPD0gZDKVpZ3BIxt3GhkKV5zCPJnz7nhTnxTa0qxnfV1rNElkcNlbPHnOlewjxS3cKimt1mOn/aIczpwl8ysIuIuaSxCO9yta67poEPf+ke8yEbqYKQhI/YrjbcjMRZj7mlyZ9GPeYx48lwtOtn2p0/LavKYmIsVkXMkwmcyM6NDcK8K4+5eE801qZWMSkdxPlCrykWAynmv1iSFNtV5Rxhu6KkS4sKFnSZj2BXDfNUkz8jEfMW+yLmWvJnmxYxz3SNMA9HfObsMWe6X/JnV13f7PaYW1WvOxHUOINeh4RIIguFzORPUfFiyhfA6kXGhbkZcdMZm+yo3v7zm/GEQysWTyaqskSaUcm01JiNmKdTvtECK0tk58nI+SLsVbLrmKdS6i2Z6GmxOWIuosW2WFm6SIq1qb26gjhf+pZFOxJbvRhIp2Z3xOpkYaWYVKuy2BIxT6WmepZtHvMszWMeyMhBwOt3vI654LcGoCkI9PMD47RLCFdlYboH8U0R3BQx1+wjYStavifzl1ckiJbrxiJF+AnBR9GvT55RbQskig1HQH1ySlWO3tKcjYWQbTei8YiFixGPuRVdSCMe8zSEuRExTOJDlOyUGDE3bH2iUmv0wU4CQHYznaCbPOZdWVlsqoKijKWLgIid0XsxL6JUotXlGVMZS2fXOov95SlVZYkPcDluZbFvPJmBpohdrtWfj3af83XMifawBz9pl+5d+sZHzKO1zXsCLMx7vJXF5kooMZ0/k0XM/c4Lc5kt34XgI085NXV67DLgj/85I4Y320ONSJKtZsU8438vc5Ggj5bTQtGIp9gKK0skYm4iamxmJ6FvifphS8miEsWgx+hYhKWrtkK+NcBVVVm6srLYVDfcddH7uIi5o8I8ldKAedaXBkzZytLskuRP+yLmnpiSifmuSP5MbGdhKwvTbYW5zQmXnXb+tKjDpdGKLLKrfgjBZyYSG5PMl+YxolKNuxypfv/rJ+aeQ7YgNlsSLmBB8mfEY15njzCX3fHTbPJnsSbMq1bD0W6OVtdkdlPEPOjCsQhx54aIucfhiLmrrCwpdKyN/1y3qclQU3YJwtouoSuEeZ0qzHem3GmE2crCdAc8umxyByPmyTp/WpFYaKYii34sMpM/zXiXY8olprFgoQ/6Q89Xn2PmT8C0b8w9j+yFXIFJYW6F1z0izE1EzM0snpI2uEoPwwu54gHWtX5Pq8GQjRFzui4KYWqLlaWLKKiZhGjTY4nbJXF9xNza5kIpjcOJ5E+ve4S5iJjX52jBBYfrmAv+bABqA0BRBjApD8jpoRFzrmPekxAfPPqVvt21w6kGrXitDuUSbRoLeZrFB1/FCmutLHQBF/+PyeYtEaFlNjpMH/6HXaB6idcuAz59FqaxyspixF9uSR1zT9Tr3mxTVRaLIuYwa2VZv9qZCKhdEfNIImqCsUQ6XjZK68Ca1vvIxuTPyPVFYPS9aLf1KRIxty75s0NVFrqlBYG4NjiS/OlLocGQ9XXM9RHzBk2YZwSa4bG6A2oKBOHBD3XAAcXAh6ND6K/FAFt6mDDniHlPQlxM6I0uotV2C3O9kHKqwZCIlpPPOllDBlmdP4WNRem0arIecEzHTROCeMPNgPJRquigpiHpCA/Zx8ikMI9WZZEkzLNzonNrl8fcaiuL12DEvMqKiHmKthraPVDOBd2unuSIeaf2BDuroBix1dhZutFVEXNnrSwxVWnIQkKBjQvujybIOlnHnHphiK7VAjsXCkrEvC4mYu4GG4vgC81nLkQ5RdFfruxZyZ8cMe9JJNrusqK6RSr+cifrmHflL5c5L+lU+kgU0SKBYzRBj5IMiYXT0heBLhHmpuuGd1kusCn6P1puZbGg66fRRl0kgISlxhIrSwoRUIpEnn67Ol6qViS8tLIj5p2VkYwkW9oghGPGkmBeaB7yRHdgO6wscee7XYuTRERseylYWSz1mOuDIV5gwHD1uAwaCdRWRqug2N35k8ZwwnVqne77z4++v8RuuA2dPxNFzJ0ulajnybUe+D3A6jbg61oPKgI9S5QTLMx7EolKKsluXpNy4mdAbSiR4KIcdoMwlx0xN2ljibH4mGn5LmkM1glzsx5zyRHzdGwsZt9HormQxK6fhstr0uKAPvApOm2FIEtFmNP70a8Ji3HbRefT7A6TmZrqdiZbdhUZJssZQf+/jPes4Yj5+u4RMbfDY66MxRttaiQW0/Flh61E34WUzg3x/9M1i0rwKuOxsUqM3mOeKyLmzvvLBW1hD+5b3fPEuB62svQk3BAxFwIo0UXVrqosXZVKlBoxTy/xs8O2qhkrSzodLa0W5gXFJquyBKIfVjKaY6W7eDF6vtAHLG2H04euZVVZMpxN/Ew1cY2afRFrlgIL/+7YJVf6WHzOlkpUxtLJ4inf7kWCTpiT3c3owt+KsaTSYMiOOubCvy0WjmIxHV9EwS4ri/j81M+DIxFz1crS5u/jOitLb4Aj5j2JRD60gM0e86JOtu/tqMpCyaelgw1YWdKNmEuwsojx0FjMjCfdaLBVwpw+fEV0sM5glE5fapOi5m1p1t9Od/FidF6K+qu3dVXmrDOdjsWAMBeJn9UWCfNUupCWasJ86Wzg61dVoW5FYl/ITRHzFBJR7fa72/ma6SzkhM9bdpdaPXphLq4NMRFzOz3mugWlfiw5edHghFg42FUuUYuYCzJZmNsKC/OeRFaCiHnIbmHeP2lELJLQZ+VYRk5UyzVS9npnkUrhf0/XKpFmDfMI6QjzdMoAJhqHrMWTiAwqW/YG50csKCNR6haXCHNf2u8DeVaWDGcTP1OtYy6EedWq2Fs7I+Z2NvRJdSx2Re/1C0MnbSypRMwpiZ2EOX2GrVpo3Tj0Nkvh94+JmNtpZdFHzHXCXFhaYqqt2VQuUfOYC9zkMe8NsDDv6VYW2Ul0XVHYia/WymZHo7cCtj0QGDBM/Xntko4ed7daWdIVxBGbhoyIucRjZDbxU9lFCEcXK+nmAciYI6MR82ILhXkqUWrbrCwpWBOElaVypTVjcGPEvLPkT6ei905XZIlJdEyykBM5CHOnWGq58Yh5UaLUBbHBBNqBFAnKdkTMxZxQyVFRTEDfAVV8rtM1SPbuW4oRc7ay2AsL856e/Bmpke18xNyyqiwbb6k21xEevFm/AD9/kFpENkOWlaXBOUEsoTKMJcnCaTZRoaRYJVFYRgJoZI4a7Fk4iffBeisi5gZyNaysYZ6KMCdRUVBibaQ8lbE45TF3wVg8bhLmnR0jum/sNur31CTN8rGEOgpzEuT9hkZ/b4cfX2/vSRQxj9Qwty8BM0vzmAtYmNsLC/PeEjG3o9tmV5UorBDmJP72PUX9/q9vgG/fSM2/KqvBkKyIudnGPuSpl+Bz7zgOv6MRc2F9CiPb/Fi22gfYbA/V29xP20lpiv3ASZmgwYVc0QDrvN2pWlkoeUx4/C2PmHs7XxhQdNjKutSd2Ufo/SEWd6LKhdV0tnhyMvnTaStLsJPzZYMJ6rGixQO9Z22ZF3+sr5voP8zWmuEx76HcBMI8YlG1J/GTyAw0KQu6sPZeYmFuLyzMexKJSjxFBIUNVVlIKIgqHIki5pFyibKqsniAA85UL+arFwGfv5B6DXBZFh9pHnOTEXMq8yW2XWUk1MlMFu4/NO2IeVrn7sSd1YWiWCxKqcqSqjDvZ13EPNVzRdhYaP6tirYJMZxsLMLGYnW0XBlLEtEn6rg31tlmBei8Qkx6O0lplUt0PGLeybyM3169nf1L5zZE2RYSfcScEHZIO2qYx8+JviqLKOEYiZjbJ449WsnElix1d4c95vbC5RJ7TblEG+qY9y1TRSK9fqLIpBB9sqL3FBEdPk6NJHzwmLHGPLIWLJGIeYN1W98peacbjTcmSoSs82XXo6MftCvmpyfMzUbMRcSemtrQwokWrLSAs9rKoi+VWCu3uZCh5M+SAdbaWBKJYdHaXCA6GFZZOIYOnT/jzt3BG6m3Zo+9VbaaBieSP532mCeZFzpnNpykfj/zZ3vGIs4XffInIawsTkTM9VYW8T6yuVRifJMht9Ux7w1wxLzXNBiyIWLeVcKbEBQy/O60CNn5cPX7L182vlUvw8qi2EjypETMFT+1Mp4MZ6w0sqr40MKMrEUUrSa+fg2Y/6e5pzJqH9FD28CiFvFHT0bfB/oyaVZFzK0slRiT/NnF4ql4oLU2lkQC9Jgr1Jbiz1ynikC7Ej87E31CmC+fC9uILOTixkJl74RFoTdaWZItnkZvqTanoxK365bZOy/iGkpzQ4t5cc7a5ekO6xsMFXSMmNvZhTRBkyHCH7DYhsbEwBHznl4u0aqES6MVWYxWk+gKSiijCzlFqv/+1vjfy0j+lGkjMdt8KVLDXFLN33TPly32VEU5fQB//BTw2yemh5KWlSVfSziknRsqjUkffmZFudEk6kgCtDWCOOUGQ1aXSkwkzAdtoAqdTXaMLZVYaYeVJUnFjyEb2y/Mk3nMhb+cdm/sEn5iLPQ+sKPTaCpjiTlGHmDzPdRvZ9kULU9kZVm1KHZsNnXZjOlCKnJCYoS5iJi3OBcxZyuLrbAw7+kNhuwU5pHEzyQRc5ljEVEO8o2aQUZNdZk2ErPlEmV2/dSPw+y8DBqp3v70PjD9+7SGEq17n2n8j0Wug9HGRskwkkQd2TmS2/HTcKJwscXNhQh99F7fCGXCjupulFis2+kxp7Jz+sUJRSGpusbqxdaPoavIsN02FpoOEZmvsGHXwozFZ/QWasIlJQdP+9bGsYRir6Hx54dtwlz32ZEo+TORRdUGstqjn61sZbEXtrJYQDgzG0HFH2bTVmWnHnMb65iLSGGyxj5WCHOzFo6ABCuLTBuJkTbrbhbmwtdduSLtoaRlZREl+mQJczNWFossJCklf5LwER5zK6PV+ii12LETczBpZ/V+Elx22DYSRWOHjFZvqVmNjBwMw2PxJUnIte+zwUOWqmdvtM86Y2RXg+yAO2qWxD/+Z01H2K6Okdh1rFge7Z1A2LajkWQnLxIxd0aY62uZc1UWe+GIuXQ8aD/5Zqw46iq1BrMjVVla7Gnqk9TK0lXEXEIiqth+TLeTYzrJnzJtJGYFsczmQumMI36rXoIgTiv5MxIxr4IUjCycIqUS5VdkibGydBa9J58sWb1IFNfIT0BNKECFF1aw/SH2Rcv1Y9FfX4Zo/vIV82AryTzmm+2u3tpRDlDPmsW2RulT3tUYt61qd6Jr6B+f2TuW+Mov9Fmif6/YnfwZT5bDEXOdlYXKJzL2wcJcNn1LFIEaIsEkVryOJn/aFDGnyIcoS5ZsC198WNGWt9HqI/GkW7tbxrxEotUm7TQyki7dFDGncyDSVEiGMG83v3iKRMwlCXMji0oLSyXGLlg6OUaRDrhLSYHAHmGeHevdFYlsdvjLk9lHnPCX68eiP19GjFctG1RF6s+v0CvR72rQcdrhUPXnXz+xzzoSPxYBRev1u712J38KRFOjbPo89zjSYEh5eV2TIX+QrSx2wsJcNiLZiXAqYu6Ex5yEEL0GWUSSiTIhPpXx+JyNmAdkRMyFKJYRMU+x0oblyZ9pJOjSMaFzgERJQ/qNXNJK/pQtzOOtT6M2Bc68ExgwwtZSiSlH7/vrhblNYlhcf2qrgAqdlcm2iLmuugVBi0QKFtD9KxfYM4bIWBK8n7feX72lZHU7LRtuQn++UDlVsjxR06epn9s+lEh5TQFdQ/W7XHYuFPRjEYsDOo9JnDvQYIjIao9+prCVxV5YmMtGlFqyq6mPQEm8ykzQ+dMmYS58tSRGkjWH0De6SHc86fq7ZSxYRJt3CTaSlCtt2BYx95m3j5CXNZ0KKDKsLMLrLstjHr+jMWYr9b2+8ea2lkqMtbJ41V2KToX5EliKPhFVHxjQJ/7abWURYlhEy2lxYnO0scNYBgwHRoxT7//dZsuGm9DbjTbaLOotF1FiW8cSd40i25d+t9dWYR7XBErULCc7i8MRc1+wFV5RipSxBRbmFgrzsB21wwX6xCv9G1hmi/U0SiWqY9G9ub0Od9yk0mHSIub1zgniSMRcsjBPp0ShpFrJHhGlNpz86YkuEmTVbY4vZyn8nyIyb1OpxJgFizKeRHPjiQrzNXZFzL2x9ZZn/hQ9l/TRc7vG4qSNJZHHfOv91NvZv8rbxenuEXNRNUiUKbQbvdgkUU7BBCesLPGLBLqWix0VssOKBa/N5RILmtbAF2xDQaP1zcGYWLgqS0+xsogPRfpA0H9wB2zq/BkRJMl9tUpsT2S9y4qYp5v8KaUqi4PJn8LSI7uOuZmFU4G8xM/YXQSDxyhPZ6mR1ekw/viI5kXxwlyUSrTIXx4zL2I8YgGj97jT+CgKaXXHTX1kOLLl3qy+L9++XxUWtZWwA0+yiLkTwlzvMe9bCozeSv05jbr+PYJIPwB/NCfJygZYqYphIYT1gSW7kj+Vseje0/TeoYUC7fpRyURRx9yBqiyH/jgZmYFeartyEBbmPcXKkixzW1br+ZRrmK/rusMliYl0u38KG4nZxMuIACU7gNec9SLdBNR0hTk9VghEN1hZhH1EesTcbzwBm6AqFBIsNQkXctldRcytE+Zd7jyRbUKUf5P1/xupyiKuQQv/tva1k46FvLm5agdSYrnNFVlixuIDho5Rx0TjoM6WvRkhhqlsJF2/yDcta/Fsdiz6wAZVZaH3DH0m2GllCceNhYR5JGKeoKiDTRQ0W3gdY5LCVhaZUPRSiDXbI+aiVGKzvAiozOZCGp6QpPGkGynWRxnNLhJk1jE3k/wpzjUSAeJCnvY40vDeS/Z1R84Vo8JcWGpk+csTLVgiVpbiWJ+3xaUSY3aekh2niI3FYn+5Mo4EVVkcEBAK+jKSol442UZk2bwMjUVnZRGRYQm1/bs94hiVluui5RZWDUrZyqJFhakSlNhlkmWDM7pIoGBTjJXFmYg54xwcMZeJuNi4MmJu8aEu7KK5kJHmKF1BUWLx96Y95jphTlFQM8lHkRriEmwkZpI/Y15f0oeb2bKNhGRftydiw/I7W8O8MysLCS/q5iiifinuHKU/nqBmCfN1USrRJnHjCmGu85j31cSwlTXcU12wCGFeY4+lx9WI61zExuKgfzkmYq6za7x1n2o/svPcibGyNACtTY4nfzLOwRFzq/zlTnnMOwhzA63EzUIXWVrV08Wli4uZ6eojiQQpbYOKJE4zW4fiwmxmAUUfuMLO4JSVJWLnkRgRlBExlyXMzdYxl931s8POkycqzPWvR+cl3a8kkVn8od7ZAsquxM8YAarr/OmUgNAvEoQwt8nf3rnHXIzFoUWCm4g0GPK6S5i36IIrtNu1ZJZzY4lP/nT6fcXYDgtzq/zl9DlhZ8Q8y8GI+YhN1NsV87tuey2lTKGkxj7pNBmSbSMxE6mWGbEXmN7R0DcXqravkY4TEXNahAphoRfmIlpO/784t6wi2dzQ4ohsXnRe2lENJaaOuajK0ux8xLzQYTGs95gXljobvXcT8SUKqxxK/EyW/OkUeo+5SP4k+vSN2htZmPcaWJhbKMwtL1HYVXMhuxoMjZyg3i6anrpvWIYwT9c7atYqETMGOTaSiAg14jGXXcM8ncWKvhIK1TGXgOkGQ7KbC8UvcOM7+sYLc6ttLJ0toES0vGKl9YuDZJ0/7axmkWgs+uo4TtlHxHXOnxXdSWJh3jFw42TEXO8x11tZnJ4XJfmzMTbIQIja5kyPh4W5TEoHxm6hO5H8mSxiTh+cyZqRpAP9j8PGqt8vTEGYy/CYy6ofHrFKmBhLnyLJ1VBMzIvM5NPIOEwunIT4aKzpetfELitLvUVWFr2NRV8FRtTy7yLPwtLjNHC4ff7yrqqy2I3+vNM3PHMC8X4WizWaEyeSUN0eMXeqVKLbIubxFWJExFxcy8i2maxxH9Pj4ORPWdCHtSZOPOuWIUwrXTclfyareZwuVCuYXpuipOuWdflwKR5zWZFis+X44rsKyu6IZ3iB0uACYS7XxmK68yeNm5IxRWt4K+ZFVGTpEDEXpRLX2Vv1Y8JOwM5HqJF8cS5b3fEzkZVF2OmowZDT4kYcE6c95hQxd3IcbkMfpaaFs5P2jETlEp1CXP9pgaDYIxtjK0w5tQvFOAJHzGXbWOrXwyMEoxPJn62dCXMLFgobbJKyjSVWbPmcF+bpNBkaru0SyEoSSqWsJe147HkiMGnX2ORPqRFzfY1sn6P2EVNWFrFAoCo7Vu0kxEfMC+yPmMf47zffU12MiHmi98UCm2qIR6xpbqjKEuz4s8wE4HTGwjaWjmLYSX85vYcSlUt0el7EAkFUZRGlEtnG0qvgiLlsYV65WufTdVvE3ILunxtMSNnGoiAzYp6u8DLrpyZhNnAD9fsls9Mbg5FINVmGtthTnUNq3iLL0pNoHAQJvbagCyLmGY5VhknYvCZH85jTLhEJYic95vS+L9PKtD53k2oNoOiaXdve+oRLpz3m8f8ziXKrGyx1tWARcEWWjgsWJ20sRCjsnoi5OE/FtTx+oeDULhTTfSLmJx+9H3775Cks+u1tfPTi3Zg0fsNOH1+Qn4fbrz4bf33xPBb//g5+eP8x7LbD5uiRpRKrVkUEn61VWTr7UJTRfj4RJEioux59OC+ZaZ09wTKPucnkwiGjVSFCHyyyBGAqOwlDR0cj2STQI1VZLBLmRgRxpBLKemcj5lYkfhJ6C5hobiWqnlCFHrpPWGhqbOiWJ+aGkj3pOJGwWL1IjbTZ6UWNlL9zPmIe03jJ6Sh1vJeaI+Ydd+ScTPyMXyTIatCW7ljEAiF+PO0szHsThsOWB+21A2689HRcddvD+HPGPJxx/EF45ZFbsOPBZ6OqurbD4/0ZGXjtsVtRub4GZ15+J1avq8Lggf1QV+/wCtWyiPnKaKkuO60sndU6JVGhNCPJsCZavmpByhe2qMfcBVYWIbaMzotsG0t818KufO3EpF2iH/4yPeaivjstPIxYWSyIVJtK/rSihnm84BPCnF6DIlvk7R42pmPilh0R2fKR9iZ7drYDJo6TU+USlfGEAJ8LotR6AUqwMFcJu0iYh5PUMXeCUBcRcy6V2KswrNTOPPEQvPLO//D6+18pP1/570ew+45b4thD9sRDz77V4fHHHLIHCgv64KCTLkcgoL4pV6yyYavXyYi52NZ3Q4MhWfaRzvzlqdpYZJdLTFuYm4yYDx8n18aSSrUaun/QBtH67UIcErKrPZAI9Waas5BYETE3srtiRQ3z+OiamHuKTtPrkDAfOibanMQOxPkyYAOHhXko1gtLOJrQpztOTiZcxnvM2criOo95ZCyUjyK7KILpiHl99D0kAiTiZ6bXYEgdUfR7wphReOiZqAAPh8P44bdp2HyCLpqnY69dtsbU6XMUK8veu2yNquo6vPvpd3j42bcRit/u0/B5PPCJE7IbEIYH7dTCl+aILsChAOg/8/ozkWGFrzsB7ZnZSjXtjEAbvHGv2aZFHjMyMzv8zixhjwftmkDNWDorpef1+3wRAerLyITP5FjaNGHub22EJ43/pz0UUObM589KeSzhvL5oJ/tOOAT/irlpvb6YE8KHEEiGejIyIvfpCZWPQiAjE2ishe+n9xHc61/R52htSnscetqUxVMm/JlZKT2vcv5ri1F/U62UsajnSnThlJnic7aXlqvHtLbS9PmVjDYajy8DnrwC5TW87S0I169HuN9QYLgqzL21FZa+58W54Q0FobyTNL+7r2K59P83FcIII0bS0PsiFJB6PqY6J3TbphPEvroqR+Yk0bz466ttnZP4eXELIYSV6xwJYX/DetvnJOY9hJDyOU3R6VSvL1bRHqaZAXwtjZFzto0W/lojO297W8rXlfo24zbRxvaMmFujOD1/3ek91Ba/m5YAQ0ehuKgAGRk+VFTFJnhVVtVg1PDBCf9mWPkAbL/lBLz7ybc44fybMWLIQNx+zTnwZ/hwz+OvJfyb/OxsFOXGlSRzMcGsPCzTtv3L/V7UZfhAccOs7Bz0K9R8pxazLDtX+aAuy8xAdtxrLg+rF8N+fYuQ1SYnutpWNAArs3LgaW/F4NZaeFL8Pyu0D86CvD4oNDE3Ya8PS7RydQMzffClMb9rvR6Q6aCwTz4KUnyehlGbgWJfmZUrUZ7tB7LlHN/CzExQrNXvz0J5grHUbDgR9K7LXbsYZctnYnlLI0LZefAE2lGelwMP4iqFpMHSkPqB1a+wCJnhrqsBBLP7YBntOoRDKPeFUz4XuiKgs7IkmpN46INtWb8hym3/lmpkSX7vLQkFEfZlILOgCDQrhV6yfjZAeUcVqz0M8pvrUGzDez7LC+XcFQxoqkamTdcaPcHsPOiLpCrXAwfGQfTLz8dShFWxRT8HWzpcC+2ivU8eRN9Vb0sjBudmAfTl0Ly4hcbsbNB+ub+uCoP79nV0LLm+DFDvaH97c0rXFytZ4/WADGBFCCJfG8vytmYENGHexxNGSYpjnJLGRsQ/VVqXWoOUOzt93eo9tLiq691cy70WHq8HVetrcfmtDysR8hn/LMSAfiU456TDkgrz+pYWNLW1obsQKtEWEc0NWFW9HmhS/WEtIWBljZwuiJ1BYoEWB0Rl1Tp44l4zoJVaWtfcDK+k8QSHaDaW1Yuxqjq1Shy06vRqUdDa9gAaTYwlLBLsQkGsXrsanjS6bgY0L3BNWzvqUxxLoFRt4BJYNEPKsaU5oTd9bYO6YCIpmuh520uHKreti2ZidVUF8OfXwHYHItxcj1WSz7GQtq27tim18yXUX/uAbaxVz38J0LyUiA6bvgysqKnt8liH+xQpixU6NyqWzItG3CURpnnxZ6E1U32/11ZXAW2xW+ANa5aj2cL3vDhfWvVVGtrbsG7pfHgcqEASzopduIVbm2255iWak3X19QjpjnnlysXwSOpCa5Sw7tQL11TYPifx89KeQpTODsItf8Gz0UyEZv/qyJzo56VJew8FGuscG4sgNOULeNrbUDvjZ9RpFs0g5av0VfPVGhpq0ZLiGA8ebdz2QpFyEuVjSiqR5zd+3Xx/jpbj1sPwO/QeMiTM11fXKT7xshLNQ61RWlKIisrE4mxdRTUCgUCMbWX+4hXoX1asWGPahc9XRzAcRtAlF5KU0Fa1JExom8JHXbrozebz2fN/bDBR9XjWrUd7xaqOLeK1OQ5QER5Z4xkwQrkJr1yQ0taMIFuLmIc8XkN/F0E0d2miN0qawou8hXS+eQ0cJy3JL7R4lrnxJyEQaIvsCHR4Xo8XKB+ljnXpP+pYf/9MrdKyeIbUcaiDUcVmgOpcpPLcedpiqW691LHohXU71XBPcK1InOexGu3ae1AqYjyalSpIbby1cyjykPVrbHnPh/VzUbEc7U55ZNvjXre1Rf75mOpQ6HWD2udMoB3ttbRIdKhbou68CNesc2xOlKEEg46+fgyNdcBr/1F2d50ekVjEhZsbnZ+fuVOVr5h3E11fNEItzSmPMT/T/LWARLmZv28L2liBrhe8hwwZuUlET/9nAXbYaoKu54kHO2w1EVOnz034N3/8PRvDhw5UHifYYNggrFlXlVCUd0vyohFDS8sTJmPsNurtP78l/iCKeHUlbpAM0qpBrFpkfW1qPSLxTkbdWaPJn2RXoFwC+h9WJD7fTdNZ8ieVxKPa6XShFiX6KEnoxVuBH9+DdIwm6IqOlxJrmBMxEe9U3ktl6q4C1i2HJYjxiLKAIvlTjx01zPVjIdY4lPiZKMnR6Q6FYjzKcQm7Y15quOunK4k09XG4uVAy9JVZuFxir8JwhuUTL76H4w7bG0ceuBtGjRiMO689F7k52Xjt/S+V399/62RcfUE0Me2FNz5FYUE+br3iDGwwdBB233ELXHjakXjujU/QY4gI87q4Mm82VGWhls8bbqp+P/tX810ljb4mJUASqxYa+tNoucSM9HYnqDJJuhhtMDRmq2iZxLhIafpj6UQMD9lIvV0xz54a1UYXT6O3VG+XzZE7DjpXhD0jlcVTvyHq7Tq961ki8Ts0VBZQL8zpnLDLOqEfi1MVWRIKc4cFhDhfnC5PqC9swBVZXImHKqg5/f7pDH3ZVaffV4ytGFZHH3z+I0qK+uLyc45HWWkRZs1dhOPPvVGpU06UDyxDSCceVq2txHHn3oCbLjsdX775oBIpf+qVD5WqLD0GEcV1ImK+4WaqUKZmN2sWJ36MmXrQnTFguFrGiUrjNVTLb6STylzL6HZp9DgJAarsTMil050EUb98ueQofZfR+xSOEdXsJ5sNCRHJ86LssWm+bmPC3OKIuaC1Ud0lEGXNaihablfHTZcIc7rWkxgmu5UbBIQ4Rk6LYf22t9OLBCYhvtm/IEhBFsk7fZZEzJ1+XzG2Yips+ezrHytfiTji9Gs63Ec2lwP/dTl6LHlCmNfFNa7x22djSRYtNyq0DNlYjEXLYyPm/vRqmMuwshix+JCNhcri0d/M/xOWCa34pj5kAbNdmBtYsIjzb+ms6MJU9lhImHcVvaexlgy0V5i3NKuilD7Y+5ZowtwmxHua3k8VFv2/hpr6eJ1vLiTG4gb7iH7h5GQ9daZz3CrKhVVOwMK8V9F9ioV3ByuLsFeIpBKrrSxUgUI0+elUmEteKIhGN2aEedoe83x5VhYt4TKledHbWKzo7Jhs8UTCl/5nWoisWSL/dROOxcAxGredejvrF2vGIha5XUXMKfGTFjU0T0Z3cUxHzLXzQNhZ7PKX68dStdo9zVHcICDEWJyOmNNOwuKZ6nvWrqZTTM+CI+a9FhtbU6IXRMxr02v1bpSNt1BfY+0yteNoMtIVw5ZEzE1G73OEMLc5+TNiY/kdlpAo4ZKi5dsfrH7/26cd/bxWkardiHYQSsvVBc68qc4Kc6v95fHCnPzk4nhUrlTzAKx87Tg84oParsVaV4tKv0sExIwf1ONCothpXvuP0yNgujPsMe+1sDCXQW5s8qd0T3cyxqRgY5EtzKmOeEGJumVsQhSkHzGXmfyZ4lhibCwWCVARMSevLn2RRYKOb8kgNQo89QtrXjedZOFx26q386dZZ2FI1VZTZrG/XBlLMPE283dvAotmAAv+gl14Z/2kNPfBtG/gOPr66U5XZZn6pfrFMN0dFua9FhbmFkTMPSISa6XHnHy3QzXv8Zzf7RPmIlpOZfu0xkWGSLcqi9TkzxQtPlbbWJSx6EsD+qiYP7CDLlpu54U5pfPFo8tvsMjGoryX2tV0ypQj5lYK8/bEwpwWTvOmwE48FAT45nW4Av25ywKCYSywsljQl4FxLSzM04Ua3gjREImYS7aOJIJEOT0/+Re7SjoLWCDMVxurXy7dY051vKXZJDKctbF0EOYZwEabOxMtT7WOOVk3aOeEPjwWTrduLKmWtKQdDcuFue4YOZ3k6Cb0pQF5XhhGDpz82WthYS4rWk6RVCEi7LCyDB+v3i6ZLb9hjEWJn+nXMffo6pjbVC6RGgpZWY1FoPeP09wIb/nvNkfLu2p2JBg5Ub2d/1dsJFn6WFJ4L1HyNb0PSSCS39tuK0tvx03JnwzTkyrG0PuJrjVWXmMZ18HCPF0i1oq6jpFYqhJBCXxWNIUZPjZqr+gKadYaDzBghKmOn5FnSEX0dbYooDklkSxDmKeSWCgWQPT/6rcWJaPU7Kb/i+aFOn1SUiUlGE6xOVouxtHVMYqcfxYn2aWSoCtsLNVropV2rC6BZ5WlqTvCwpxh5ENW0WdvcL7qEmM7XC5RctdPBaOtxM0sBsTW/dLZ1jf10TeTodbwJH46qwLTCZ5IvW4TwnzHw9TbmT/JqVASL0A32x3Y/4zYYyYEKNXpthqxaBGdXJf944zQ6co+kp2rNplKdcdGylj8ziZ+KmNhK0uXVhYW5gwjD2ocqO8uzPQKWJjLLpVI6Fe4VtQyF2JxzdLUGu3IqmPeX1sMVKw0L4zNWlkGb6TWbCdx9NP7sESA7nQ4MGHHqKec4tjDxtojQPXCb9Rm6u0iC73bnY6ji2M0dIxaOaZyleUt6Cn5s9OIOY1j/Pb2lA6MEeYcMU+4k8ALFoZhmLRgYS67uRBpBSofJqJIVkTMh49L3cZCpGMf0UMWizRbgHvMRu93PFS9nf69vE56etHnz4z618drTXPKBqsLL8qIX7kAtgkc6iJJUAk+J+jKymLnLkJXXXQn7awuGGmB+vd31o6FI+aJ4Yg5wzCMNFiYWxEx11s2MtwgzFOsPtIVkcoX5hupRD3mBuZl6Gj1fyZh9POHkIY++TO/ONZXTgsuIUBXzLWnuY8+uZCq7dA2phuFuRO7CInOXbLU7HSE+v0P76S2eyRjLAR7zJN4zHnBwjAMkw4szGUlf+o95jLKAiajqJ9aKYSenwSjzIYxqVpZqNOoSSILFiPzsv0h6u20b+X67fTlEvsURe/3eoGx2+oWQDYI0Hjh51S0XD+ORMeIGkxRYirtCi2bY/1YIscos+PvdjhULZ9JNfX//Nr6sbCVpWth3soRc4ZhmHTgqixWJH/qhbnsiLkQiyvmq1U70o06pgrZPKhudboR84jHPEUrCwlmilyTEPzlI0hFHzEv0IQ5vQ75lifsAPQtM7YzIdOr65S/vCthPkyX32BhlZoux0KLg833UL//8uXY7pNWj4VgK0vHnR66HtlxHBiGYXowHDG3ysqSaitxo0RsBAbEoozovfCXk8UiHR+p0bGICiXk8a5fD2v8yxlRK8uCv9Uxkm2HKtCQPSKNHQJTc0PjWvqPPa/Z2TgSLZ6MlOmUQaLkT4qeH3yuWjpz3lT7xsIR88495uwvZxiGSRsW5tKSP5MJc8mbEsLnbaTBj4yxiNelSGkaGK5jTh0wiXl/Wtu8JrIbsBxYMC36GKUcpQV16BOOR5ub5fPUGrZO0Zn1adi41Mt0SsCTqKLQXieqtcupIsxnz8E22GPe+U4P+8sZhmHShoV5OlAlj8zsJFYWC7p/UoSQPOYElaqzNWIuEj/TFOaROuZa86XOoIj1sDHq91Z03dQLLfLtExSVn/lz9H67/OX68ThpY+nsfFHyG0rU39PiwZaxxL2PqDTixJ3VKO37j3bYqbJ2LBwx7zRizv5yhmGYtGGPuYxoOUU347dxrfCYF/VXBS35WxuqU/87GXXMI6US07N1RDzmyngyOu9qtsEE9TG0CLGiQonoKinmVgjzxTPVaCz56hfbmIRJpSApOk8NlJwkUlEoo2MtedEF1coOm8nKJdIx2vtk9ecf31UbMNmJvmoOe8yjiPc0R8wZhmHShoW5BRVZ0m49n4ySgert+tXG/i4yFpOdP2lxIV47XWEe0xW1C2G+0WbWRcuTRsyr1ftfug3IzgNqKmAbVIfb6lrcRs6XeCvLoJHq7Sobaron8phvsgOQmaX673/+ALYjFrgEC/MEwpwj5gzDMOnCwlxycyFLrSxCHFetltukpStKB6uRevo/jUTqu2pG0ln5Rnq9DSZaK8wpOk1zQ8dILKBEgiklufZWkllZhDBfudD+sVD0fsjG6vezfgbCNvn+E42FBChXH4nCyZ8MwzDSYI+5BRVZLKvKUjLInDA3Uztccv1ygYfEcCqed2oqRA1kyFJipRDUR81JpDdZ3KSmOxCxPmXEVkKhhEujicfpIhaVWbnRhYEd9dM720ngxM9YOGLOMAwjDRbmFtQwN91Ix7KIuXuEecp11UU1lvl/WVsVRW9PoEWAXRVY3Ewi69OA4eouBll9ZJet7ITIApden3Y26PWd2s0Q72lO/IyFhTnDMIw0WJhbHTGXaWUpFsLcQEUWGVVZROJnmhVZOoynMyvLSM3GsoCEuYXoE0BtFJyuJtFuT8RfbmO0PL7WPLHcoWg5sXqx2nnWitKd3RlR2rO53umRMAzDdHvYY54OuZ15zCVbWSg6T9YO8nNWr7NPmFOUVAjzNGuYR8ejS4yl8nfbHgC8fX+08gq1fS8sU/9Xq20L+og5RWOZxOeLU8Jcf3yctLGIBfjDk517fbcy9Uv1dvqPTo+EYRim28MR83TIL+ykKksKdg0zNpbaio5ixUphTo2F/FlqB0yjFppk6G0+VJOa2qtP2Cn6+/JR6m3FCuu3x/VVYThinrzzp9MRc8Gyufa+PtM1tKD+4qX0E8MZhmEYFuam2XzPaF3nBM1+pHf+NOsvJ9KpEDNYE8krF8jzX+uFn+i4OVzrKKkX5sprWow++ZMj5irx5y7tYFBjIdrBIDuHrWNpj41YG7VxMQzDMEw3goW5Gch+QW3BiR/eBSqWdxIxz5RckWVVeo1RyJpihPIN1duV8yGLyNxQJL6gWP1+wDDVqqMX5qvsFuYcMU+YAyCi5ZUron5im/DoI+YcLWcYhmF6OCzMjTJqErD/Ger3v3+mdiBMgCsj5mai5kKYr5AoksXckI9cLBQ8XmDoGPVnqsBhV8Q8xsrCEXOF+HPXifrl8WNxOvGTYRiGYWyAkz+Nsv3BgNertk//6tWkD5MuzIvTEeZx3TZTJb9YszAEgdWLjL9uV+MpGhB7/7CxqjimXYam+mgyqJXoFy11HDFXiM+PcMpfTsREzFmYMwzDMD0bFuZmLSW/ftKp51pq508SqhRdNivMqUsiiWuKRhsR5sJSQvXLZVoYhLWmuL/2c0Ad17Ax0RrVdolAUS6ROjkmKHvZq+tS0y4GHZeBI5wT5rRAo3Oezr+Klfa/PsMwDMPYCAtzoyULs3LUJLiazksWpl0ukewck3YFfv806lOnyihmawXTeIwK88EbWmMpEVVZijRhvvBvYMNNgbLB0cZCEj3tnSIWUA21UUHa29HvsFC0PDMbaG12JPHSQwump67R2r5z8yeGYRimZ8PC3AjFA3QlC3XiJQFpl0uk2t6jtwJGbwnM+kW9Lx1hRJFhSrY0EzGXLZJFlLqon3q7dqlqm6HILEXN7fKXE+I4ceJnYvvIpF3U24XT1Z0XJ+AFE8MwDNNL4ORPM8I8Be9z2p0/CzXRmtMH2GJP9ft06ojra4enAkXpRWOhFZKFuRiL2AmorQSW/qP7fQhYJdHTnooI5cTPxEKYFofE3986NhyGYRiG6S2wMLdamJu1svQt7SiK0xHmAYPjoeg1iXgSrNSGXCKRuRHUVgFLZ0V/pvKTdpXlE1YWFuax6BeWNRXAktlOj4hhGIZhejwszC0S5oYj1HrI00uRcuKNu9WSjOuWAfOmGn+uDuPxOVa/PGFddaKuElg+LyoG7bKxCIsGVWOZ/6d9r9kd0C+eqAIR+7sZhmEYxnLYY+5GK4vohknJnpR0R02M6EuGZSPViLno+Cmzfnki0Ue2FRLGZJ8gcT58rL1l8Sjx9OGL7Xu97oI4RpR8OeNHp0fDMAzDML0CFuap4vFEq4hYbWURpRHJey07Sp1qBN+qxE9lLLrkwobqqKf5k6eAoaOBf36X/5qMMcT5u2iGdCsTwzAMwzCJYSuLEc83idpAW0qNaNJqMCQi5lKFeXvq46EFSG6B+r9SxRQrrSzkL498X6lFZ9k24TitTert32RjYRiGYRjGDjhibtjGsjYl4ZhWucS+VkTMDSwURP3y1Uu6LAuZlt9d+MsZ9/H5i0C/ocDcKU6PhGEYhmF6DSzMrUj8jOn8qZUENEJfLWIu00JgZKFgpY1FPxbZiw9GHktmqV8MwzAMw9gGW1ksE+YGPd2JSiVaETH3piLMN7Smfnn83BAszBmGYRiGYRRYmBsV5tVkZYG1HnMrhXlXEfOsXKCs3NqyhfrkT73HnGEYhmEYphfDwjxVikxaWUiYU0WXVKHyinl9nfOYDxoJeLzqAqSpTt7rJxoLwRFzhmEYhmEYBRbmqYpl4ftev9p4d0sjJRMLtGg51S9vaYQ0Ui3fGKlfbpG/XBmLzsrCpfgYhmEYhmEUWJinQmE/NYpMQrmp3rgwN1KZxQobS4ww9znX8TO+KktjnVqSkWEYhmEYhmFhbkXiZ6RjInW1NBoxt0yYp9D5kyw3ZGWxquNnZCyaMK+tsO41GIZhGIZhuhkszC0S5oqrPFIy0W+iVKJsYZ5ClZiywUBWjmqjqVwBq/BSUml9NXf4ZBiGYRiG0cF1zFOhtNxQRZaYyLA/y1hlFhExr5EszAMpdP4UNpZVC4Cwdd03PTSPD11k2fMzDMMwDMN0RzhirqewDNjpcGC7A3V3eoAR480lRAZEiUITVhbZEXPh6yZhTq9x8HnAoA1iHzNqkvU2FoZhGIZhGCYhLMzjSyJufzCw+Z7REocDhwN9ClV7x7I58n3dyaqyyK7vHdAJ8y32AsZuDRx2EZCdp95Piw8S5qEg8M9vcl+bYRiGYRiG6RIW5nqWzlYrr5AQF7aOkVoUefEMVbQaQV/LPBW8PiC/0JrESH0d84FapDy/CNj7JPV19zxRvW/ql0DVKrmvzTAMwzAMw3QJe8z1kPBeMA0Yvz2w8RbAinlRe8eCvw0/nScQQNiIlaWgRC3L2N6acllGw8KcPO8DhkXvH7sNkNMHKBkINNQAP7wj93UZhmEYhmGYlOCIeTxzp6i3G22uRs4HjlB/XmhcmBuuyiIqsljRpl4I8wHDVXFOOwM/vqveJzz037yuWnYYhmEYhmEY22FhHs+iGWrEmhJBtztIvW/VQnPt6fW+7lToW2ZN4qd+kUD/F7F6CfDTB8DqRerPy+cBM3+S/7oMwzAMwzCMdcL85KP3w2+fPIVFv72Nj168G5PGa37sLjh47x2xatqHeObea+FaqBPlwunq95vtpt6SvcUMRpM/hWiW3VxIGUucP371QtW68/YDwK8fAx88Kv81GYZhGIZhGOuE+UF77YAbLz0d9zz+KvY+9mLMnrcYrzxyC0qK+nb6d4MH9cP1l5yKX6fOhOuZp9lZyO8tQ5inamXpN0S9rVhp7vVSGYtglRYpr1+vWljqLLDPMAzDMAzDMNYJ8zNPPASvvPM/vP7+V5i/aDmu/PcjaG5pxbGH7Jn8RbxePHz7pfi/R1/B0pUGm/Q4ASV6Ck82dahcu9Tc8xi1svTXkjLNvl5niP9HICwsDMMwDMMwTPeryuLPyMCEMaPw0DNvRe4Lh8P44bdp2HzCxkn/7pKzjkHl+lq8+t4X2GqzcV2+js/jgc/roP090Ir2pbMR3mACvIumI8PnM/Tnfu3x3lAQZCDx+TPh6+I5wjl90E5VWejvq1bCY/A1uyIUDiEizeurkdlcD0h+jVTnRdwyPCfJ4HlJDM9LR3hOEsPz0jvnpb7NQN8Ujcb2jJhbo2T20Lm04lxpi7cVJ8DQUSguKkBGhg8VVdUx91dW1WDU8MEJ/2arSWNxzCF7Yq+jU2/Bnp+djaLcXDhJ61//Qw1CKJ79PfyFWm1xg+R4gQaqgtinAIVdPEdz+UZYQwektgKDc7MB+pJIc0628vxEbuVy9Df5P8mgX36+Y6/tVnhOEsPzkhiel47wnCSG56V3zcsU8UFvgn+qtAaHBil3Tk50u3NlcVWVs3XM83Jz8MBtl+DyWx7C+prUq5rUt7Sgqa0NjlJTAyycjXUm/pRWV3QgW5rV0oN17QE00vN1QnDDYvV29RKs7OKxZgjlRp+zddk8S14j1XlZV1+P9hRWjb0BnpPE8LwkhuelIzwnieF56Z3zcvDoFsN/Q5FyEuVjSiqR54+zvabA+3PkBhJ7+7liSJivr65DIBBEWUlRzP2lJYWoqIyNohPDhwzA0PL+eP7+6yP3eb1qq/tlU97DjoecjaUrOi7vguEwgj3gDROiCi/0/3i9Xf8/WuJneM2SlLY6DNMeXegEVy5wdH7pBLfkf+zG8JwkhuclMTwvHeE5SQzPS++al/zMuEIPBiBRbubv24LG7TPdiXabzxVDwrw9EMD0fxZgh60m4LNvflXu83g82GGriXjutY87PH7B4hXY9fDzYu678vwTlUj6Df95AqvWWFAW0E2IhMuMzK4f22+oervOgsRPIqB7s61ZYs1rMAzDMAzDMKYxbGV54sX3cN+tk/H37AX4a+Y8nHH8wcjNycZr73+p/P7+Wydjzboq3PHgC2hta8fchcti/r62vlG5jb+/RyLEcFdVWUi4lwxSv19jkTCvWQesXwOsWwa0NlnzGgzDMAzDMIx9wvyDz39UapZffs7xKCstwqy5i3D8uTeicr3qWS4fWIZQOGx+RD0IT6TBUNw0U330LfYClswEKlYAZYPJ4wM01qpfVi0SHr/CmudmGIZhGIZh0sZU8uezr3+sfCXiiNOv6fRvJ99wH3oNEStLnP9qkx2APY4DaipUsWxl/XKGYRiGYRimW+BgsXD0IitLnDAfs5V6W1gGjN8e6K/5y9f2AnsPwzAMwzAMY3+5xF5PJGKum+bsXGDY2OjP2x0EtKi+e46YMwzDMAzD9F5YmNsdMR+1qeo5r1qtivSiftHfsTBnGIZhGIbptbCVxY7kT73HfPSW6u0/vwK/fhK9v60FqF5r8wgZhmEYhmEYt8DC3A4ri6jKkpkNjBivfj/nD+Cvr4BGrSPquuUAV7NhGIZhGIbptbAwt5KAEOZaxHzkRLVmOdUTpzKJ1I3zp/fV3y2e6dw4GYZhGIZhGMdhj7mVxFtZNt4yGi0XTP0CWDKLbSwMwzAMwzC9HBbmdnX+pEg5RcyJuVNiH1e1yv6xMQzDMAzDMK6CrSx2lUvcZHsgM0ttKrRmsdMjYxiGYRiGYVwGC3ML8QhhnpUL7Hi4+v0fnzk6JoZhGIZhGMadsJXFDisLVWOhr8pVwJ9fOz0qhmEYhmEYxoVwxNyO5E/BV68AoaBTo2EYhmEYhmFcDAtzKxFWFmLh38Ci6U6OhmEYhmEYhnExbGWxEurm2dqslkv86lWnR8MwDMMwTA/mmb9yDf9Nps+H8kLg/TnZaAvqOpUzjsDC3Orkz9fuAqihJ5dEZBiGYRiGYTqBhbnVrFrk9AgYhmEYhmGYbgB7zBmGYRiGYRjGBbAwZxiGYRiGYRgXwMKcYRiGYRiGYVwAC3OGYRiGYRiGcQEszBmGYRiGYRjGBbAwZxiGYRiGYRgXwMKcYRiGYRiGYVwAC3OGYRiGYRiGcQEszBmGYRiGYRjGBbAwZxiGYRiGYRgXwMKcYRiGYRiGYVwAC3OGYRiGYRiGcQEe9Ns87PQgGIZhGIZhGKa3wxFzhmEYhmEYhnEBLMwZhmEYhmEYxgWwMGcYhmEYhmEYF8DCnGEYhmEYhmFcAAtzhmEYhmEYhnEBLMwZhmEYhmEYxgVkOD0At7L1ZuNw7kmHYZMxIzGgXwlOnXwbPvvm18jvS4sLce3FJ2PnbSahb34f/PrnTFx31+NYvGx15DHDBg/ADZeciq0mjUVmph/f/PwnrrvzcVSur1F+P3hQP0w+42hsv9VElJUUYm3Ferzzybe4/8k30B4IoLfOi55MfwY+fun/MG7jDbDn0Rdi1tzF6M1zsvuOW2DymcdgzIbD0drWjl+nzlRez43YNS8bDB2E6yefgi0njYXfn4F/5i/Bfx5+CT9PmQG3cf6pR2C/3bfDqOHlaGltw5S/5+C2+57DwqUrI4/JyvTjxktPw0F776h8/+3Pf+Hq2x+N+Z/LB5ThjmvPwfZbTEBjczPe/PBr3P7A8wgGQ5HHbLvFeNx06enYaORQrFpTgfufegNvfPAV3Ihd87LvbtvipKP2xbiNNlDOp7kLl+H/HnsF3/3yF3rzuSLYctIYvP3UHZi7cCn2PPoiuBE754U+fyafdSwO328XlJUWYV3letz7+Gt47f0v0Zvn5dD9dsa5Jx2uXHvrGhrxzU9Tceu9z6K6th49cU5uveJM5b2x8ahhWLB4ecL3Bn0m33712Zg4bkOsr67FM699hEeee8fUuDlinoTcnGzMmrcY19zxWMLfP3PvtRhW3h+nTL4Nex1zEVasrsDrj/0bOdlZyu/p9tVHb0E4HMaRZ16Lg0++QnmTP//A9fB4PMpjRg0fDK/Xiyv//TB2Pfw83HT3UzjxiH1w9QX/Qm+eFz3XTT4FayrWw83YNSd0gXng35fg9fe/xJ5HXag87t1Pv0Nvn5fnH7wBvgyf8ph9jrsYs+ctxgsP3qAsdt3GtpuPx3Ovf4wD/nU5jjn7emRk+JT/UfzPxE2XnY49d9oKZ11+Fw477Wr0LyvG0/dcHfk9XTPo/6O5OOjky3HR9ffhqAN3x+XnHh95zJBB/fHigzfipz+mKwvap17+AHffcAF23nZTuBG75mWbzcfh+1+n4YQLblbOlZ+nTFfOp/Ebb4DeOieCgvw83H/rZPz4+99wM3bOy+P/uRI7bDUBl978AHY85Gyce9V/Y0Rdb5wXEqgP3DoZr733BXY5/DzluSaN3wj/veF89MQ5Ebz2/hf44H8/IBF98nKU512xeh32OW6yski59KzjcPzhe8MM3GAoBVZN+zAm2kerxB8/eFw5KectXKbcR0Lh769ewJ0PvohX3v1c+QB86aEbMWanY9HQ2Kw8Jr9PLv75/lUce84N+OG3xBe/c046FP86cj9se8AZ6O3zsuv2m+OmS0/D6Zfdge/eecS1EXM75sTn8+K3T57G/z36Cl597wt0N6yal+LCAsz89mUccsqV+P2v2cpj8nJzMP/nN3D0WdclfZ+5heKiAsz85mUceupV+O3PWcr/N+Obl3De1Xfj4y9/jizgv3/vURxw4mX4c8Zc5X3xwgPXY9M9T45EdWhBf+1FJ2OTXU9Qdtuuvegk7L7jltjtiOiH5aN3Xq6Ir+PPuwlux6p5ScQ3bz+sfODe+8Rr6M1zQufHomWrEAqFsM+u27g2Ym7XvOyy3WZ49K7Lse3+Z6CmrgHdDavm5ex/kUbZF9sdeGbktU495gCce8rh2GLvU9DT5kTPpWcfm/C9QfNx5fknYtLu/4q8p6658CTlsTsdeg6MwhFzE9AWKNHa2ha5j6J6bW3t2HLTsepj/BkIh6HcJ6DHh0JhbKU9JhH5ffJQ47LtICfmhWwOtAK/4Lp70NzSiu6KrDkhO8ig/qUIhUP4/LX78NcXz+Olh27CxiOHojfPy/qaOixYvAJHHribEgWhBQx9kFRUVWP67AVwOwV98pRb8Z6fMGYUMv3+mAXFgiUrsGLVOmw+cbTy8xYTRmPOgqUxW620/UqiW5wPm08YjR9+mxbzWt/+8qdyf3fAqnmJhxaDfXJzusU118o5Ofrg3TF08ADc8/ir6G5YNS977bI1ps9agHNPPhxTP38OP7z/GG6YfCqyszLRm+dl6vQ5GDSgFLvtsHnks3r/PbbH1z9OQU+ck1Sg6yoJff1C99uf/8SoEYPRN199TSOwMDeBOHBXX3iSMun+jAycd/LhGDSgDP1Li5THTJ0xF03NLYqHlgQDfZFXlrZS+pUWJ3ze4UMGKivPF9/+DL19Xu675WK8+Oan3UJc2TEnw8oHKLe0PXbfk2/gXxfegtr6BsUPWljQB735XKHIOFkRKEq++Ld3cOYJB+P4c29CbX0j3AyJwpsvP0OJ9JPXmehXWqTkDtTFjb1ifQ36adacstJCVFTF5h6ID1LywYrb+MfQz/QB63ZhYeW8JNqhzM3Nxgef/4jeOicjhg5UonsXXPN/CX3nvXVeyGZHQQLyFZ92yW248b9PYv89t8Md1xiPgPakeflj2j84/5r/w2N3XYGlf7yL6V+/iPqGxqSWxe4+J6nQL9H1totrT2ewMDdBIBDEaZfejpHDBuGfH17Dwl/fwnZbboKvfpyiRPOI9dV1OOuKuxTvEgmGuT++joL8PorQpK3CeAb0K8bLD9+Ej774Ca+88zl687ycduyBimfrwWfeQndH1pyQ94+4/+k38MlXP2PGPwsx+Yb7lCjzAXvugN78HqKEm8rqWmV7cv8TLsVn3/6G5x64XrlYuhka9+hRQ3HOlf9xeii9cl4O3XdnXHLWsTj7irtQVV2L3jgndF15+I7Lcfejryg2lu6GlecKzQ1dX8+/5m5MmzkfX/84FTfd/bSyO+f2xa2V87LhBkNwy+VnKNYv8lOTrZAKWdx17blwM7d3o+stV2UxCQkj8hmRR4kqQZCI+OjFu2MivJTpTz4s8sEGgkFlVTbtyxewbOWamOeiZIM3n7xdyRi+/NaH0NvnZfutJmDzCRtjye+xGc2fvnwv3vn0W1x8/X3obXNCFXuI+QuXR/6mrT2ApSvXoHxgGbojMuaFErP22GnLGB/6jNsfxU7bTFKSlh561p2Lu9uuOgt77rQlDj31aqxeVxW5f11ltVIZgCLb+ihOWXEh1mkRmYrKGmw6fqOY56PtZPV31ZHb+ORX+pmek6oTuBWr50Vw8N47KsmwZ15xp+vzEKycEwqATBq3obLjdNtVZyv3e70eRZQum/KeIroogbg3nitrK9djzboq1Dc0RR4zf/FyZW4G9i+JqR7Vm+blglOPxB9//4NHn39X+ZmqYDU3t+K95+7CXQ+/pLxOT5qTVFiX6Hqb5NqTChwxTxN605KgoO3AiWNH4X/f/tbhMeSDpYO+/ZYTUFrcF59/+3tMpPytp27HjNkLMPnG+5UVem+fl+vvegJ7HHWhkuxJXydecLNy/9lX/gd3PfgieuOcTP9ngSKoRg4vjzyWLB1DBvVTMsG7M+nMi8iuF1F2QTgUgsfbscqPWz4k9tltW6WKzPJVa2N+R8e5rb0dO2w1MXLfyGHlSkRq6t9zlJ+nTJ+D0aOGoaSob+QxO207SZmfeYuWRXyg+udQHrPNpsr9bsWOeSEO2Wcn3HPzRTj36v/iqx/c7Yu1ek7ovUcVwcS1lr5eeOszJW+Dvo9PfutN5wpZNgaUlSjVpfTPEwwGsXptVNz1tnmha2447nobDAWV20SV1br7nKQCXVepPDB9Juvnjd5HZiyVHDFPAr0ZSSgIhpT3x7iNR6CmtgEr11TggD23V7Y/V66uUOpX3nLFGfjsm99i6uFSQs38RSuUx1FyAD3miZfej5RbUkX5HVi5ah1uufcZlBQVRP423q/Um+aFnkdPY3OLcrt0xeqY1W5vmhOKBr/41qe49JzjsGptpeLPPuekw5TffeRSf6wd8zJ1+lzU1jUqZd7ufeJVtLS0KSWq6LW++uEPuI3brzkHh+67E065+DblmIooCwkkWnjR7avvfqFUI6IEpfrGJuWDZcrf/0REEs3PvEXL8eBtl+Df9z2LspIiXHneCXjujY+VXRTihTc/wynHHIDrLj4Zr733pbILdeCeO0QWub11Xsi+QvkrN/z3SeXvxOuI1+iNcyL8toKq9TVobWvrcH9vm5d3P/lO6TNy7y0XKVYf2rWj8r1Uw9yNu052zcsX3/+O/15/vlKJhBIcacefvNv0HGJntyfNicj/y8vNVuaDbEz0OUbMW7hcSfikssVki/u/Gy/Ew8+9jdEjh+L04w7CjXc/BTNwucQkUHMOSqyL5/UPvlK8veSDpsSh0pJCrKuoxpsffY37nng9JiuXEmqOOmh3FPbtg+Wr1inJjCQqBPQ7+pBIxKBJB6K3zks8tHr9/ZOnXVsu0a45odX4NRechMMP2AXZWVn4a+Zc3PDfpyLlBnvrvEwYOwpXnX+icktJpCQoyP9ITS/cWDYyERffcF+k+Y9oeHHwPjtpDS/+VBpe6BfrZF+689pzsd3mmygJstQE5LYHnuvQYOjmy07HhhsMxeq1lbjvyddd22DIrnmh3cntttgk6TnZW8+VVErC9cZ5odJ5/77qTGw5cSyqa+uUJGFqXuZGYW7nvFCRihOP3BdDB/VXihCQ3em2+5/DmnXre+ScvJXkurHVfqcpQbIODYZq6vDsqx8pIt0MLMwZhmEYhmEYxgWwx5xhGIZhGIZhXAALc4ZhGIZhGIZxASzMGYZhGIZhGMYFsDBnGIZhGIZhGBfAwpxhGIZhGIZhXAALc4ZhGIZhGIZxASzMGYZhGIZhGMYFsDBnGIZhGIZhGBfAwpxhGIZhGIZhXAALc4ZhGIZhGIZxASzMGYZhGIZhGAbO8//NFuANy/Ar+gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "last_window_used = (last_window_extended.index[0], last_window_extended.index[-4]) \n",
    "dummy_values = (last_window_extended.index[-3], last_window_extended.index[-1]) \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "# Plot using matplotlib directly with proper date handling\n",
    "ax.plot(data.index, data['y'].values, label='train')\n",
    "ax.plot(predictions.index, predictions.values, label='predictions')\n",
    "ax.plot(\n",
    "    [dummy_values[0], dummy_values[1]],\n",
    "    [last_window.iloc[-1, 0], predictions.iloc[0]],\n",
    "    color='red',\n",
    "    linestyle='--',\n",
    "    label='Gap (Dummy values)'\n",
    ")\n",
    "ax.fill_between(last_window_used, data['y'].min(), data['y'].max(), \n",
    "                facecolor='#f7931a', alpha=0.4, zorder=0, label='Last window used')\n",
    "ax.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "Some forecasting models, such as <a href=\"../user_guides/forecasting-sarimax-arima.html\">ARIMA and SARIMAX</a>, do not have as much flexibility in terms of changing the last window values. In these cases, forecasts must be made from the last available data to the desired forecast horizon. The forecast values for the delayed data may be discarded as they are already past values.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "For a better understanding of how to deploy Forecaster models,  visit <a href=\"../user_guides/forecaster-in-production.html\">forecaster models in production</a>.\n",
    "\n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "skforecast_py12",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
